The Decentralized AI Ecosystem in Healthcare: A Systematic Review of Technologies, Governance, and Implementation

Abstract

This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, governance frameworks, and real-world applications. A systematic literature review was conducted, analyzing peer-reviewed studies from PubMed, Scopus, and Web of Science to map the current landscape of the field. The primary objective was to synthesize the current research on decentralized approaches in healthcare, including core approaches like federated learning and blockchain-based AI models, as well as emerging concepts such as agentic AI blockchain-based AI models and DAOs, to comprehend their application in clinical and operational settings. The research assesses the maturity of these implementations, ranging from pilot programs to large-scale organizational settings. It also identified the key computational and technical methods and platforms used and the key benefits and challenges influencing their adoption. The findings underscore the pivotal role of the decentralized paradigm in addressing the fundamental limitations of traditional AI, including data privacy, trust, institutional silos, and regulatory complexity. Insights are also offered for healthcare providers, technology developers, researchers, and policymakers aiming to navigate and leverage decentralized AI to build more equitable, efficient, and collaborative healthcare systems.

1. Introduction

The healthcare field is currently at a critical juncture, poised for a revolution driven by artificial intelligence (AI), where the potential of AI to transform patient care is considerable. Improvements in diagnostic accuracy, acceleration of biomedical research, and optimization of clinical operations can characterize this transformation. However, this potential is fundamentally constrained by the prevailing paradigm of centralized AI, which faces significant and often insurmountable limitations within the complex healthcare landscape. The conventional approach, which necessitates the aggregation of substantial quantities of sensitive patient data into centralized repositories, directly conflicts with an expanding network of data privacy regulations, including Europe’s General Data Protection Regulation (GDPR), the United States’ Health Insurance Portability and Accountability Act (HIPAA), and China’s Personal Information Protection Law, among numerous others. These regulations, conceived to safeguard patient autonomy, render the extensive data aggregation essential for developing sophisticated AI models a legally and ethically contentious undertaking [1,2,3].

The healthcare sector is characterized by deep-seated institutional and technical silos, as well as regulatory hurdles. The patient data is dispersed across a multitude of incompatible electronic health record systems, research databases, and proprietary platforms, which poses a significant challenge to data access and integration. Moreover, the intrinsic protected nature of numerous advanced deep learning models is incongruent with the tenets of clinical practice, wherein transparency, explainability, and accountability are of the utmost importance. Clinicians and patients have a legitimate expectation of comprehending the rationale underlying an AI-generated diagnosis or treatment recommendation. This is a requirement that many monolithic, centralized systems are not equipped to fulfill [2,3,4]. The barriers are present within a context of unprecedented systemic pressure, in which aging populations, projected workforce shortages, and persistent health inequities across global geographies are just a few of the factors contributing to the strained state of healthcare systems worldwide.

In response to these challenges, Decentralized AI (dAI) is emerging as a transformative paradigm shift. In contrast to the conventional approach of transferring data to a centralized algorithm, decentralized methods involve relocating the algorithm to the data itself. This enables AI models to learn from distributed datasets while ensuring that sensitive information remains securely within its original institutional and jurisdictional boundaries. This fundamental principle serves as the cornerstone of a rapidly evolving array of technologies and governance models, meticulously designed to ensure a new era of secure, collaborative intelligence. Studies frequently propose novel cryptographic methodologies to enhance the security of Federated Learning (FL), develop new algorithms to improve the efficiency of distributed training, or explore optimization strategies for specific model architectures, in environments with limited resources [2,3,4,5,6].

A significant corpus of literature comprises descriptive case studies and pilot project reports. These offer invaluable real-world evidence of the possibilities, but, by their nature, they are focused on a single implementation, a specific governance model, or a particular clinical use case [3,5]. While individual technologies and isolated case studies are well-documented, a holistic understanding of how the underlying technologies, novel governance structures, and real-world implementations interrelate is lacking. This fragmentation has the effect of hindering strategic decision-making and slowing the broader adoption of these powerful solutions [5,6,7].

The study is driven by four objectives, in which the initial objective is to identify and categorise the primary dAI technologies, computational methods, and system architectures, focusing on core enablers like FL and blockchain while assessing the maturity of emerging concepts like Agentic AI. Secondly, it is essential to comprehend the clinical and operational use cases, as well as the maturity level of these implementations, ranging from proofs-of-concept to full-scale, real-world deployments. Thirdly, a synthesis of the key benefits and challenges reported in the literature, such as enhanced privacy and improved data access, is required, as well as a consolidation of the principal challenges and risks, including interoperability standards, data quality variation, and governance alignment. Finally, the fourth objective is to distill actionable implementation frameworks, guidelines, and best practices for both technology implementers and data providers. These are derived from documented case studies and expert experience.

The research questions (RQ) that guide this study were defined in a solid alignment with the defined objectives: RQ1: What are the primary dAI architectures, technologies, and use cases being applied and tested in the healthcare sector? RQ2: What is the current level of technological and implementation maturity of these dAI use cases in healthcare? RQ3: According to the literature, what are the most significant reported benefits and challenges associated with the adoption of dAI in healthcare? RQ4: What actionable implementation frameworks, guidelines, and best practices can be distilled from the literature for technology implementers and data providers?

To accomplish the aforementioned objectives and answer the RQs, the employed systematic literature review (SLR) methodology follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The review will also encompass a comprehensive search of major academic databases, including PubMed, Scopus, and Web of Science. The collected data will be analyzed through both a qualitative thematic synthesis and a quantitative bibliometric analysis. Tools such as VOSviewer version 1.6 will be used to identify research trends, thematic clusters, and intellectual networks.

To provide a more formal analytical lens for this complex environment, this review adopts the concept of a technology ecosystem from the information systems literature. A technology ecosystem is defined as a network of interacting actors (organizations and individuals) that are connected through a shared technological platform and co-dependent relationships to co-create value. In this context, dAI is not viewed as a monolithic technology but as the foundational platform. The various components like FL for computation, Blockchain for trust, and DAOs for governance constitute the platform’s core, upon which actors such as hospitals, researchers, patients, and technology developers interact.

2. Background and Theoretical Framework

2.1. The Imperative for Decentralization: Overcoming the Limits of Traditional AI in Healthcare

In a Decentralized Autonomous Organization (DAO), stakeholders ranging from participating institutions to individual researchers are empowered to exercise democratic governance over the protocol, deliberate on model approvals, and oversee incentive management. This establishes a truly holistic framework for the responsible and effective utilization of collective intelligence, where control over data, computation, and governance is transparently shared among its participants [8,9,10,11].

The dAI ecosystem is not a single technology; rather, it is a confluence of interlocking computational methods, technical Architectures, and trust-enabling protocols. These components function collectively to facilitate secure, collaborative intelligence. In the standard FL process, a central server coordinates the training [10,11,12,13]. The process initiates with the dissemination of a global model to all participating nodes, such as hospitals. Each node subsequently trains this model locally on its private data for a few epochs, producing an updated set of model parameters or gradients [11,13]. These local updates, which encapsulate the insights derived from the local data, are subsequently transmitted back to the central server. The server then aggregates these updates, frequently employing a rudimentary algorithm such as Federated Averaging, to generate an enhanced global model. The dissemination of this novel global model to the nodes is a recurrent process. The pivotal innovation lies in the exclusive local institution-based storage of model updates, ensuring that sensitive patient data remains within the institution’s boundaries, thus preserving privacy and adhering to regulatory frameworks such as GDPR [13,14,15,16].

The FL paradigm can be adapted to a variety of data distribution scenarios. Horizontal FL is employed when participating institutions possess analogous data features but vary in terms of patient cohorts. Conversely, vertical FL is applicable when institutions possess disparate data features for the same group of patients—a hospital and an insurance company sharing data about the same individuals without disclosing their complete datasets. This adaptability enables the application of FL to a broad spectrum of healthcare challenges, ranging from collaborative efforts among hospitals in the detection of rare diseases to cross-border research on population health. It even extends to cooperative pre-clinical research among competing pharmaceutical companies. To further enhance privacy, FL is often combined with cryptographic techniques, such as homomorphic encryption, which allows for computation on encrypted data, or mathematical frameworks, including differential privacy [16,17,18,19].

The ecosystem is evolving to include more sophisticated forms of distributed intelligence beyond the confines of federated training of a single model. Agentic AI is defined as a system composed of autonomous agents that can plan and execute complex sequences of actions to achieve specific goals. In the context of decentralized healthcare, this phenomenon translates into the development of sophisticated applications. Local-first agents can operate primarily on a user’s device or a hospital’s local server, performing tasks such as personalized health coaching or clinical workflow automation with minimal external dependencies, thus maximizing privacy. Federated agents can collaborate across institutional networks while preserving data locality, thereby facilitating complex, multidisciplinary decision support. A prime example is the concept of a virtual tumor board, where multiple specialized AI agents, one for radiology, one for pathology, one for genomics collaborate to provide a comprehensive analysis for a cancer patient, mimicking the process of a human multidisciplinary team. This vision of collaborative intelligence is further expanded upon through its integration with other key innovations. The potential for a DAO to oversee the network in its entirety is a notable feature, as it enables member hospitals and research centers to engage in transparent voting processes concerning protocols, agent validation, and resource allocation. Finally, this framework extends to Decentralized Physical AI (dePAI), which is defined as the integration of decentralized AI systems with real-world, physical hardware. This connection enables AI agents to directly and autonomously control devices such as a robotic microscope for pathology or an imaging device creating a full-stack, autonomous framework from data acquisition to physical action [20,21,22,23,24,25,26].

These trends towards more complex intelligence are coupled with the rise of multimodal models, which are designed to process and integrate multiple types of data simultaneously, such as text from clinical notes, imaging data from MRIs, time-series data from sensors, and structured data from lab reports. In a decentralized environment, these challenges are compounded, necessitating the establishment of protocols to ensure proper modal alignment that is, the accurate correlation of disparate data types across institutions. Additionally, the management of heterogeneous data quality, an inherent feature of clinical settings, becomes a critical concern. In addressing these challenges, frameworks such as DeepSeek’s Multimodal FL framework are being developed to enable the training of powerful multimodal models across hospital networks in China [25,27,28].

The computational framework for these systems is provided by FL and agentic AI, while Blockchain and other distributed ledger technologies (DLT) offer a foundational trust layer for the ecosystem. Beyond the realm of cryptocurrency, the potential of blockchains extends to providing an immutable, transparent, and auditable record of transactions and interactions. In the domain of healthcare, this technology can be employed to manage data provenance, track patient consent, and create secure audit trails for AI model usage. This is imperative for ensuring regulatory compliance and fostering trust among stakeholders. More advanced cryptographic methods, such as Decentralized Identifiers and Zero-Knowledge Proofs, are built upon these principles. These methods enable patients and providers to control their own digital identities and verify credentials without revealing unnecessary private information. This trust layer must be understood not as a mere passive record-keeping system, but rather as an active enabler of new governance and economic models that are essential for the ecosystem’s sustainability [29,30,31,32].

2.2. The Governance and Economic Layer: New Models for Collaboration

The success of dAI is contingent upon the development of robust governance structures and appropriately aligned economic incentives that can foster collaboration among diverse, and sometimes competing, stakeholders. One of the most innovative approaches to governance in this new ecosystem is the DAO—a governance structure that operates via rules encoded as smart contracts on a blockchain, rather than through traditional hierarchical management. This collaborative decision-making process, characterized by transparency, empowers a group of stakeholders to collectively determine the course of action. In the domain of healthcare AI, DAOs present a compelling solution to the prevailing governance challenges. These challenges arise from the need for direct participation from a diverse set of stakeholders, including patients, clinicians, researchers, and developers, in decision-making processes concerning the development, validation, and utilization of AI models. These tools can be used to manage shared computational resources, transparently allocate research funding, and even automate aspects of regulatory compliance [33,34,35,36].

The practical applications of this model are already becoming apparent, particularly within the context of the Decentralized Science (DeSci) movement. DeSci initiatives employ DAOs and tokenization to establish novel funding mechanisms for research, to establish frameworks in which patients can possess and govern their biomedical data while enabling its utilization for research purposes, and to construct transparent, community-driven alternatives to conventional peer review and publishing processes [37,38,39]. In the context of AI, DeSci can facilitate the creation of collaborative training datasets governed by scientific communities, the development of open-source model architectures, and democratized access to advanced AI tools for researchers worldwide [40,41,42]. The decentralized paradigm necessitates a transition from conventional software licensing to more dynamic and collaborative economic frameworks. The market is undergoing a discernible transition toward platform-based models and value-based arrangements, wherein revenue is contingent upon quantifiable clinical or operational enhancements rather than on the number of software seats purchased. In ecosystems with multiple contributors, novel incentive mechanisms are imperative. These can include formal contribution-based value sharing, where benefits are allocated based on the quality and quantity of data or computing resources provided, and tokenization, where digital tokens can be used to represent ownership, governance rights, or access privileges within the ecosystem [43,44,45,46].

Despite its immense potential, the dAI ecosystem is in its infancy and rife with challenges, debates, and unresolved questions that represent critical gaps in both research and practice. A fundamental tension exists between the push for Sovereign AI and the need for global collaboration. In the contemporary global context, numerous nations and health systems are progressively perceiving their health data as a strategic national asset, thereby asserting local control over data, computation, and governance [33,38,40]. This phenomenon is exemplified by Europe’s establishment of the European Health Data Space and China’s implementation of proprietary AI models within its national digital infrastructure [41,44]. While decentralization can respect these sovereign boundaries, it creates a dilemma: the most robust and unbiased AI models are often those trained on the most diverse, global datasets. A novel solution to this challenge is emerging from initiatives such as Switzerland’s recently announced sovereign AI program. The Swiss AI Initiative has developed a large language model, Apertus, that is both fully open-source and transparent. This model was created on a national supercomputer, offering a third way that reconciles national sovereignty with the benefits of global collaboration [47,48,49,50]. The model has been designed to support multiple languages and is founded on principles of public interest, ensuring that while the infrastructure is nationally controlled for security and accountability, the resulting AI is open to the global community for innovation and scrutiny. This approach enables a nation to preserve its digital autonomy without resorting to technological seclusion, thereby establishing a reliable foundation for both domestic applications and international scientific progress [51,52,53,54]. The central and persistent challenge of navigating the balance between local control and global knowledge sharing is of particular significance. Beyond the realm of geopolitics, substantial technical and operational challenges persist. The absence of universal interoperability standards poses a significant challenge to the integration of decentralized solutions with heterogeneous hospital IT systems. The inherent variation in data quality across different institutions has the potential to compromise model performance and introduce biases if not managed with the requisite degree of caution. Distributed systems introduce unique security vulnerabilities, such as model poisoning or membership inference attacks, that require novel mitigation strategies. The substantial technical intricacy and coordination overheads necessary for the establishment and maintenance of a federated network can be prohibitive for organizations lacking substantial resources [53,54,55,56].

The governance conundrum is arguably the most intricate gap. While the theoretical underpinnings of DAOs are promising, their practical implementation in a regulated domain such as healthcare is in its nascent stages. The establishment of fair and effective decision-making frameworks among diverse stakeholders with competing interests, the creation of clear lines of accountability in a system with no central authority, and ensuring that these novel organizational structures comply with existing legal and regulatory frameworks are all unsolved problems. This results in a final, pivotal gap: the human element of user acceptance and trust. While technology can be meticulously designed, its successful integration hinges upon addressing concerns regarding patient privacy and clinician perceptions of AI as an opaque, unpredictable system that potentially compromises patient autonomy. Addressing digital illiteracy and establishing reliable trust in these intricate systems poses a pivotal last-mile challenge [57,58,59].

3. Methodology

3.1. Research Design and Methodological Approach

The research employs an SLR as its core research design, guided by a multi-layered conceptual framework developed to navigate the complexities of the dAI ecosystem in healthcare, conducted and reported in accordance with the PRISMA 2020 guideline for systematic reviews [60,61,62]. The PRISMA checklist is provided as Supplementary Material. This framework organizes the domain into four interconnected layers, beginning with the foundational technology layer, which encompasses core enablers such as FL, Agentic AI, and blockchain. The second, the resource and process layer, describes the mechanisms of decentralized data management and computation. This dynamic environment is further influenced by the governance and economic layer, which encompasses innovative structures such as DAOs, governance, dePAI, DeSci initiatives, and novel incentive models. The final implementation and application layer signifies the tangible embodiment of the ecosystem within specific clinical use cases and documented case studies. The selection of an SLR is substantiated by the highly fragmented and rapidly evolving nature of this field. The current state of research in this field is dispersed across multiple disciplines, including computer science, medical informatics, and policy studies [51,63,64,65].

3.2. Data Collection and Study Selection

The SLR data were obtained from three academic databases—PubMed, Scopus, and Web of Science. This selection ensured an accepted coverage across biomedical, computer science, and interdisciplinary research, thereby minimizing the risk of omitting relevant studies. The keywords used in the full search strategies are exhibited in Appendix A. The search was deliberately constrained to peer-reviewed articles published between January 2023 and July 2025. This timeframe was not chosen to discount foundational work, but to specifically capture the current state of the art in a field characterized by explosive growth and rapid innovation. The aim of this SLR is not to re-evaluate the seminal concepts of FL or blockchain, but rather to map their recent, practical application, integration, and the emerging governance models that have gained critical mass following the maturation of these base technologies. This focus allows for a timely snapshot of the application-oriented research landscape.

A search query was developed by combining keywords from the core concepts of the research framework using Boolean operators (AND, OR). The search terms were grouped into three categories:

• Decentralization Concepts: The following terms are relevant to the subject: “decentralized AI,” “federated learning,” “distributed AI,” “agentic AI”, “multi-agent system,” “decentralized autonomous organization,” “blockchain,” “distributed ledger technology,” “local AI”, “dePAI infrastructure”, “Edge AI”, “Edge computing”, “off cloud computing”, “dePAI” and “DeSci.”
• In the context of Healthcare: The following terms are relevant to the field of healthcare: “healthcare,” “medical,” “clinical,” “hospital,” “patient data,” “drug discovery,” “medical imaging,” “data protection,” and “population health.”
• AI Model Concepts: The following terms are used in this text: “foundation model,” “large language model,” “LLM,” and “multimodal model.”

To ensure the relevance and quality of the included literature, a set of inclusion and exclusion criteria was applied during the screening process. Inclusion criteria include the following:

• The study must be a peer-reviewed article published in English.
• The study is required to describe an implemented or tested application of a dAI system or framework.
• The application context must pertain to the domains of healthcare, biomedical engineering, or clinical practice.
• The study is required to report empirical results, which can be quantitative (e.g., performance metrics) or qualitative (e.g., case study findings, user feedback).

Exclusion Criteria:

• Theoretical, conceptual, or mathematical models that lack practical implementation or empirical evaluation.
• General AI applications that are not explicitly decentralized.
• Review articles, editorials, conference abstracts, opinions, and non-peer-reviewed literature.

The selection of studies was conducted through a two-stage screening process, overseen by independent reviewers, to minimize selection bias. Initially, the titles and abstracts of all articles retrieved from the database search were screened against the established eligibility criteria. Studies that did not meet the established criteria were excluded from the analysis. Secondly, the full texts of the remaining articles were reviewed to determine their inclusion in the final publication [66,67,68].

A total of 178 studies were identified, and the titles, abstracts, and search terms were reviewed according to the eligibility criteria; of these, 87 studies were eligible for full-text screening. Next, the full-text versions of potentially eligible articles were independently examined by two reviewers to exclude those that did not meet the eligibility criteria [51]. The two reviewers discussed differences of opinion about study eligibility until a consensus was reached on the studies to be included in the review. Additionally, the quality of the included studies was appraised using quality assessment tools specific to particular study designs. The study selection process is summarized in the PRISMA flow chart in Figure 1.

The SLR employed a two-stage, mixed-methods analytical approach. First, the full cohort of 87 eligible studies was used to conduct a comprehensive quantitative bibliometric and temporal analysis to robustly address RQ1 and RQ2, mapping the high-level intellectual structure and maturity of the field. Second, to address RQ3 and RQ4, which required a deep, granular synthesis of specific architectures, benefits, and challenges, a purely superficial thematic analysis of the highly heterogeneous 87-study cohort was determined to be unfeasible and would ‘dilute the clarity’.

To address RQ3 and RQ4, which required granular synthesis of specific architectures, benefits, and challenges, a second purposive-stratified selection was conducted from the 87 eligible studies. Selection criteria for the in-depth subset required studies to: (a) describe a fully implemented or empirically tested dAI system (not purely simulated); (b) represent at least one of the three primary architectural pillars (FL, Blockchain, DAO); (c) provide sufficient technical and implementation detail to enable meaningful cross-study comparison; and (d) collectively ensure coverage across diverse healthcare domains and geographic contexts. This yielded 13 studies. The authors acknowledge that this purposive selection introduces potential selection bias; however, it was deemed necessary to achieve analytic depth without sacrificing rigor.

3.3. Data Analysis

A standardized data extraction form was developed and piloted on a random sample of five studies to ensure consistency. Data were independently extracted by two reviewers, with discrepancies resolved through discussion until consensus. No automation tools were used in data extraction. Contact with the original study investigators for data confirmation was not undertaken. The following key data points were also extracted:

• Study Characteristics: author, year of publication, publication type, study design, and geographic location.
• Healthcare setting: the specific problem that is being addressed; the rationale for choosing a decentralized approach.
• The technical solution: the specific decentralization approach (e.g., FL, blockchain), the technology stack used, the system architecture, and the privacy and security measures implemented are the primary factors to be considered.
• Subjects of governance and implementation: the governance model, economic framework, and change management strategies that have been employed are of particular interest.
• Impact and Outcomes: quantitative results, including performance metrics, cost savings, and scale, as well as qualitative benefits, such as improved clinical outcomes and enhanced collaboration.
• Challenges and limitations: technical, operational, and regulatory challenges that were encountered during the implementation process.

Before synthesis, extracted data underwent several preparation steps. Categorical variables (e.g., dAI architecture type, healthcare domain) were harmonized to a standardized taxonomy derived from the conceptual framework to ensure consistent classification across studies. For studies reporting quantitative performance metrics, results were extracted as reported without conversion or transformation, given the heterogeneity of metrics used (accuracy, F1-score, AUC, etc.) and the decision not to perform meta-analysis. Where data fields were missing or ambiguously reported in primary studies, the corresponding cells in the extraction form were marked as ‘Not Reported’ rather than imputed. No summary statistics requiring conversion were generated.

Given the qualitative and mixed-methods nature of this systematic review, formal quantitative effect measures (e.g., risk ratios, mean differences) were not pre-specified or calculated. The included studies were highly heterogeneous in design, reporting performance metrics that varied widely (e.g., accuracy, F1-score, AUC for FL studies; qualitative governance assessments for DAO studies), precluding meaningful pooled effect estimation. Synthesis was therefore conducted through thematic analysis and bibliometric mapping rather than statistical meta-analysis.

The choice of thematic synthesis over meta-analysis was driven by the substantial heterogeneity in study designs, outcomes, and reporting formats across the included literature, which precluded meaningful quantitative pooling. VOSviewer was selected for bibliometric analysis due to its established use in mapping intellectual structures in emerging research domains. The thematic coding process followed a framework approach: extracted data were first deductively coded against the four layers of the a priori conceptual framework, followed by an inductive pass to capture emergent themes not anticipated by the framework.

The methodological quality and risk of bias of each included study were also assessed to weigh the strength of the evidence. To address this need, a custom quality assessment checklist was developed, given the anticipated heterogeneity of the included studies (e.g., technical evaluations, case studies, comparative analyses). The checklist encompassed criteria for the clarity of objectives, the appropriateness of the methodology, transparency in reporting, and the validity of the conclusions. Concerning the strategy for synthesizing the data, a multifaceted approach was employed to analyze the extracted data and address the RQ. The descriptive analysis was conducted by means of fundamental descriptive statistics, which were then used to summarize the characteristics of the included studies. These characteristics included, but were not limited to, the number of publications over time, the geographical distribution of research, and the most common healthcare domains and technologies studied. For the bibliometric network analysis, the VOSviewer software was employed to conduct a bibliometric analysis of the literature.

This involved the following steps: first, mapping keyword co-occurrence to identify thematic clusters and intellectual trends; second, analyzing citation networks to identify seminal papers and influential authors; and third, examining co-authorship networks to map collaborations between institutions and countries. The thematic synthesis was achieved by means of a thematic synthesis of the extracted data. The qualitative and quantitative findings from each study were coded and organized according to the a priori conceptual framework (i.e., the technology, resource/process, governance/economic, and application layers). The process entailed the identification of recurring themes, patterns, and relationships within and across the studies.

4. Results

4.1. Evidence of Importance and Preliminary Findings

A review from the inclusion phase indicates the utilization of a set of distinct yet frequently complementary technologies in the development of dAI systems within the healthcare sector. The preliminary technologies identified during the screening phase underscored the significance of FL, smart contracts, DAOs, and the interplanetary file system concept. (IPFS). The primary value proposition of this technology lies in its capacity to train AI models on decentralized data without the necessity of transferring the data itself, thereby ensuring the preservation of patient privacy.

As part of the supporting technologies, the results from the screening phase also highlighted the relevance of the IPFS concept, which is frequently referenced for off-chain storage of large data files (e.g., medical images). In this approach, only the hashes are stored on the blockchain to reduce costs and enhance scalability. Several papers have proposed a hybrid architecture that incorporates FL for model training and a blockchain layer for several purposes. These purposes include auditing the training process, rewarding participants (e.g., hospitals), and maintaining a transparent record of model updates. This architecture directly addresses both privacy (FL) and trust (blockchain). In addition, the integration of blockchain technology, smart contracts, and IPFS constitutes the prevailing architecture for decentralized data management. Patient data (EHRs) are stored off-chain on IPFS, access is controlled via blockchain-based identity and smart contracts, and a log of all access requests is immutably recorded [68,69,70].

Furthermore, this review’s findings highlight a critical gap in the literature. While our search protocol was designed to capture emerging concepts such as Agentic AI and dePAI, the systematic screening found a significant scarcity of mature, empirical studies focusing on them. This ‘finding of absence’ is as significant as the trends identified, suggesting that while these concepts are part of the theoretical ecosystem, their practical application in healthcare remains a nascent field ripe for future investigation. Their limited inclusion in our analysis is therefore a direct reflection of the current state of the published literature.

The initial analysis yielded several compelling examples of clinical applications, which were subsequently linked to the mapping of the most relevant use cases. The primary application of medical image diagnosis, particularly as it pertains to the utilization of AI in healthcare, is the segmentation of brain tumors, the detection of the novel virus through computed tomography scans, and the execution of other diagnostic imaging analyses. Subsequently, the processes of drug discovery and clinical trials have employed decentralized platforms to securely manage and share trial data, track provenance, and facilitate collaboration without compromising intellectual property. Furthermore, the literature was examined to identify additional applications in operational and administrative contexts. It was determined that patient record management is a critical component in the development of patient-centric EHR systems, wherein users are granted full control and sovereignty over their data [19,47,51].

Among the salient reported advantages are enhanced privacy and security. This is the most frequently cited benefit, as it directly addresses patient confidentiality and GDPR/HIPAA compliance concerns by maintaining data localization. However, some of the insights were associated with enhanced data access and collaboration. In this regard, dAI has been instrumental in facilitating the breakdown of institutional data silos. This has enabled the generation of significantly larger and more diverse datasets for the training of robust AI models without the need for centralized storage of sensitive information [65,70]. However, there are substantial technical challenges that have emerged from the initial observations. Chief among these challenges is the issue of scalability, which is particularly salient in the context of blockchain. The inherent limitations of blockchain on transaction throughput persist as a significant concern, not only in terms of scalability but also in terms of interoperability. A challenge pertains to the absence of standardization in the integration of dAI systems with existing legacy hospital IT infrastructure. This is exemplified by the lack of adherence to HL7/FHIR standards, which are crucial for seamless integration of digital health systems. In the state of Florida, the management of non-IID (not independent and identically distributed) data from disparate institutions poses a substantial statistical and technical challenge, with the potential to compromise model performance (

Table 1. Validation of domain and key initial insights collected.

Domain	Key Insight
Technological Foundations	The technological architecture primarily consists of two pillars: FL for privacy-preserving machine learning, and Blockchain technology, with its associated Smart Contracts, for data integrity, auditable access control, and governance.
Primary Application Areas	Prominent applications are categorized into clinical domains, with a focus on Medical Imaging Analysis, and operational domains, targeting EHR Management and the integrity of the pharmaceutical Supply Chain.
Implementation Maturity	The field remains at a nascent stage of development. The body of literature is dominated by Conceptual Frameworks and Proofs-of-Concept, with a discernible absence of large-scale, longitudinal studies conducted in real-world clinical environments.
Core Motivation	The principal driver for research and development is the potential of dAI to overcome the limitations of centralized systems. This is achieved by enabling Enhanced Data Privacy and establishing Trust and Transparency, which in turn facilitates collaborative research across previously siloed datasets.
Principal Barriers	Significant barriers to widespread adoption are multifaceted. They include technical challenges (scalability, interoperability with established health information standards), regulatory and legal ambiguity, and the intrinsic complexity of designing robust and equitable decentralized governance models.
Emerging Governance Models	An emergent trend within the literature is the exploration of DAOs as a novel governance framework to create equitable, stakeholder-driven ecosystems for health data sharing and collaborative research initiatives.

).

4.2. Geographic and Time Data Analysis

Figure 2 illustrates the sharp escalation of academic interest in dAI within the healthcare sector between 2023 and 2025. The modest publication count in 2023, with a single entry, is characteristic of a nascent research domain establishing its foundational concepts. A surge is observed in 2024, where publications reached a peak of 16. This significant increase reflects a growing recognition of dAI’s potential to address persistent challenges in modern healthcare, particularly concerning data privacy, the breaking down of institutional data silos, and the development of trustworthy, collaborative machine learning models. This period signifies a critical inflection point, likely driven by the increased maturity of enabling technologies such as FL and Blockchain. Although the data for 2025 currently indicates 5 publications, this figure represents a partial count for the year and should not be interpreted as a decline in research interest. The exponential trendline superimposed on the data corroborates the expectation of a continued and robust growth trajectory. Collectively, these publication dynamics underscore the rapid transition of dAI in healthcare from a niche theoretical concept to a significant and rapidly expanding field of scientific and practical inquiry.

Figure 3 data indicates a concentration of influence within a few key countries, alongside emerging contributions from others. The United States is the predominant leader, accounting for approximately 47.6% of the total, with 248 citations. This dominance underscores the nation’s pivotal role in the advancement of dAI methodologies. This high citation volume may reflect the nation’s robust biomedical research ecosystem and the significant academic focus on addressing challenges associated with complex data privacy regulations, such as HIPAA. This strategic positioning of the United States as a central hub for innovation is particularly evident in the development of sophisticated FL algorithms and blockchain-based frameworks for secure health data exchange.

A substantial research axis is evident in Asia and the Middle East, with India and Saudi Arabia emerging as highly significant contributors. The analysis indicates that each country is responsible for 109 citations, collectively representing nearly 42% of the total research impact. The representation of India at this event is indicative of the nation’s escalating prominence in the domain of health-tech. This prominence suggests a strong research focus on scalable dAI solutions for healthcare data management, a relevant topic for the country’s large population. The equally robust contribution from Saudi Arabia aligns with its stated national goals for digital transformation and advanced technology as part of its broader economic vision to establish a state-of-the-art healthcare system. The analysis further reveals that significant contributions from Pakistan (32 citations) and China (13 citations) underscore a deepening and widespread engagement with dAI technologies across the continent to address regional healthcare challenges.

In contrast, contributions from developed nations in Europe and Oceania appear more modest in terms of citation volume. The United Kingdom (8 citations), France (1 citation), and Australia (1 citation) demonstrate engagement in the field; however, their current impact, as measured by citations, is less pronounced. This discrepancy in citation volume could suggest a different publication focus, perhaps with a greater emphasis on regional regulatory and ethical frameworks necessitated by regulations such as the GDPR, as opposed to large-scale, application-driven studies. Despite their modest scale, these contributions underscore the global character of the research endeavor and a pervasive recognition of dAI’s significance. This analysis of the geographic distribution of citations reveals a global yet concentrated research landscape for dAI in healthcare. The leadership of the United States reflects its established research and development infrastructure, while the high research impact from India and Saudi Arabia suggests these nations are significant new hubs for scalable, technology-driven healthcare research. This dynamic underscores the necessity for a concerted global effort to synthesize technological innovation with the multifaceted regulatory and clinical requirements inherent to diverse regions. Such collaboration is pivotal in facilitating the responsible and equitable advancement of dAI within the global healthcare ecosystem.

4.3. Bibliometric Mapping and Influential Publications Analysis

A bibliometric analysis of the selected publications was conducted to generate a keyword co-occurrence network, which was then used to map the intellectual structure of the research domain. The analysis identified key terms and their relationships, revealing the thematic evolution and core concepts driving the field of dAI in healthcare. The nodes in the network are representative of keywords, with their size corresponding to the frequency of occurrence of these keywords in the literature. The links between nodes indicate co-occurrence relationships, where thicker lines signify a positive association. A temporal overlay has been applied, with the color gradient from blue to yellow illustrating the evolution of keyword prominence over time, with yellow indicating the most recent research topics. The analysis indicates that the most recent research, indicated by the use of yellow, is marked by the recurrent co-occurrence of terms such as “federated learning,” “edge computing,” “internet of things,” and “blockchain.” These challenges are inextricably linked to foundational issues, such as “interoperability,” and the implementation of novel models, including “large language models.” The concept of “federated learning” occupies a central role in this contemporary cluster, exhibiting significant interconnections with “privacy preserving AI,” “learning systems,” and “blockchain.” This nexus underscores a predominant emphasis on cultivating methodologies that facilitate collaborative machine learning across institutional boundaries, while circumventing the centralization of sensitive patient data. Moreover, the term “interoperability” occupies a critical bridging position in the network, connecting foundational topics with emerging ones. Its linkage to both “healthcare data interoperability” and newer technologies underscores its role as a persistent and fundamental challenge in deploying effective dAI solutions within complex healthcare ecosystems.

Figure 4 illustrates the conceptual architecture of the dAI in the healthcare domain, revealing at least three distinct thematic clusters. The most recent and prominent cluster, colored in yellow, is centered on the practical application of privacy-preserving technologies, including FL, blockchain, and the IoT. A secondary, more mature cluster, delineated in purple on the left, and a more significant cluster, signify the foundational principles of AI. A third cluster, also in purple but on the right, relates to the healthcare intellectual origins of decentralization in agent-based systems. This network analysis reveals a discernible intellectual trajectory, illustrating the evolution of the contemporary emphasis on applied dAI in healthcare from its foundational principles in AI and multi-agent theory. The landscape is undergoing a transition towards addressing the pressing issues of data privacy, security, and interoperability.

An analysis of the keyword co-occurrence network reveals several emerging trends within the domain of dAI in healthcare. The temporal overlay, indicated by the color gradient, shows that the entire research landscape depicted is extremely recent, spanning 2024. This suggests that while blockchain in healthcare is the foundational topic, the most current research is expanding to integrate cutting-edge models like Generative AI and to address specific concerns like privacy through specialized techniques such as FL.

According to Figure 5, the network is dominated by two central keywords, “blockchain” and “healthcare,” which serve as the primary hubs. The link suggests that the correlation exists and blockchain solutions are already increasingly connected with healthcare, which might suggest the evolutionary perspective and penetration of blockchain in healthcare settings. Keywords such as “privacy” and “artificial intelligence,” though smaller, are connected to this main cluster and represent evolving priorities within the discourse. The recent emergence of “generative AI” points to a new research frontier focused on leveraging advanced AI models within these secure, decentralized frameworks.

Figure 6 illustrates the co-occurrence network derived from the keywords of the chosen publications. The analysis pinpoints the most influential and interconnected terms within the research domain. The network reveals that “blockchain” and “healthcare” are the two central nodes, and their significant link strength confirms their role as foundational pillars of current academic discourse. These terms are tightly integrated with a cluster of keywords that define the problem, including “challenge,” “opportunity,” and most notably, “digital identity challenge.” This indicates that the literature heavily focuses on leveraging blockchain to address the critical, persistent issue of digital identity management for patients and providers within the healthcare ecosystem.

The temporal evolution of research topics, represented by a color gradient ranging from blue to yellow, reveals a clear progression in research focus. Foundational concepts from this period, such as “digital identity challenge” and general “review” articles, are depicted in blue, indicating that they set the stage for subsequent work. In contrast, the most recent keywords, highlighted in yellow, include “privacy,” “issue,” and “generative AI.” This temporal shift shows that the research conversation is shifting from defining broad challenges to investigating specific, nuanced issues, with a pronounced emphasis on data privacy and integrating novel technologies, such as Generative AI, into decentralized frameworks. This analysis illustrates the interconnectedness of core technologies, practical challenges, and emerging innovations. The keyword “solution” acts as a critical semantic bridge, linking foundational technology (“blockchain”) and the application domain (“healthcare”) to specific problems (“digital identity challenge”) and enabling technologies (“IoT”). Peripheral terms such as “federated” and “integrating blockchain” represent distinct technical strategies discussed in this field. Overall, the network provides clear insight into the trajectory of the research, which has pivoted from problem definition to exploring advanced privacy concerns and integrating next-generation AI.

As illustrated in Figure 7, the paradigm of governance is undergoing a transition from a centralized model to a distributed network of stakeholders. In contrast to the aggregation of sensitive patient data into centralized repositories, a practice that has been increasingly restricted by regulations such as GDPR and HIPAA, the concept of dAI is presented as a new operation of increasing importance over the past year. This concept is represented by the yellow color indication of a high influence level. The figure presents the conceptual evolution of organizations’ approach to changes in the relationship between traditional implementation factors and emerging decentralized governance structures. The data reveal the presence of three distinct clusters, which appear to demonstrate an intellectual progression. At the core of this paradigm, the keywords “technology” and “organization” function as the pivotal nexus, thereby establishing a linkage between long-established organizational practices and innovative decentralized frameworks. On the right, a cluster colored in blue represents the foundational and established considerations for any technology adoption “initiative,” including “integration” strategies, “risk” management, and “resource” allocation. The temporal coloring of these elements serves to substantiate their status as the initial, more traditional aspects of the discourse.

In contrast, the cluster on the left signifies the most recent and pioneering domain of exploration. This entire cluster is dedicated to the concept of the DAOs. The emergence of keywords such as “dao creation,” “knowledge dao tool,” and “organizational assessment” signifies a substantial shift in focus. This evolution transcends the mere integration of technology into an existing organizational structure, signifying a paradigm shift towards the innovative utilization of technology to devise entirely novel models for governance, collaboration, and value creation. The co-occurrence network offers a clear trajectory in both academic and strategic conversation. The text delineates the transition from addressing conventional challenges of technology “integration” to proposing radical new forms of organization. The analysis indicates that the demands for transparency and stakeholder control in healthcare AI are driving not only technological innovation but also a fundamental rethinking of organizational structure, governance, and interaction itself. In this context, the DAO is emerging as a key conceptual framework for future exploration.

Figure 8 reveals four distinct thematic clusters, each representing a crucial facet of the research domain, from foundational principles to the emerging research frontier. The temporal overlay demonstrates the developmental trajectory of the field’s focus and its respective priorities. The analysis identifies two foundational clusters that form the bedrock of the research area. The initial cluster, characterized by its dense purple coloration, is situated at the base of the network and corresponds to the Clinical and Governance Foundation. The present area is grounded in practical application, featuring keywords such as “hospitals,” “clinicians,” and specific use cases such as “cardiology” and “heart disease.” It is imperative to note that this paradigm is intricately interwoven with the foundational blockchain governance mechanisms indispensable for deployment, encompassing “decentralized identity (DID),” “DAOs,” “smart contracts,” and “consensus mechanisms.” The second foundational cluster, which is represented by the teal color, signifies the Theoretical Underpinnings of the field. This cluster encompasses established concepts from AI, such as “multi-agent systems,” “autonomous agents,” and “cognitive systems.” It also incorporates critical systemic considerations, including “ethical aspects” and “cybersecurity.”

The cluster occupies a central position within the network structure, functioning as the primary dAI Technology Hub. This cluster represents the contemporary nexus of the field, bridging foundational theory with emerging applications. The most central and influential keywords in this field include “federated learning,” “interoperability,” “edge computing,” and “internet of things.” This underscores the prevailing emphasis in contemporary mainstream AI research, which is predominantly oriented towards the development and integration of these fundamental technologies.

Moreover, the integration of contemporary concepts such as “explainable AI (XAI)” and “retrieval-augmented generation (RAG)” signifies that this fundamental principle is engaged in the active assimilation of state-of-the-art advancements from the broader AI landscape to address issues of transparency and model capability. The most recent research trajectory, represented by the yellow cluster on the left, is known as the Computational Performance Frontier. The cluster’s emphasis on keywords such as “load-balancing,” “resource allocation,” “optimization,” and “distributed systems” indicates a significant development in the field. A marked shift in research emphasis is evident, with a transition from conceptual validation to the exploration of practical implementation challenges. Having demonstrated the viability of dAI in healthcare, the scientific community is now addressing the subsequent challenges related to efficiency, scalability, and computational resource management. These elements are imperative for the successful implementation of dAI in real-world clinical and operational settings.

4.4. Detailed Final Selection Analysis

Table 2 analysis is primarily built upon three core technologies: FL, Blockchain, and DAOs. FL is the most mature, with applications focused on privacy-preserving machine learning for clinical tasks like medical imaging and EHR data analysis, often using methods like FedAvg. Blockchain technology is typically applied to enhance data integrity and security in operational use cases, such as managing clinical trials and securing patient records, with smart contracts being a key computational method. DAOs represent the newest frontier, focused on governance rather than direct data analysis, with conceptual applications in governing civic medical data trusts and funding life sciences research, DeSci.

Table 2. Final selection of publications analysis of architecture, use cases, and methods.

Author, Year	Architecture	Use Case(s)	Key Computational Methods
[66]	Primarily horizontal FL. Architectures include client-server (centralized coordinator) and, less commonly, peer-to-peer (decentralized).	Medical Imaging (brain tumor segmentation, diabetic retinopathy, chest X-ray analysis), EHR Data Analysis (mortality prediction, disease diagnosis).	Models: CNNs (U-Net, ResNet), LSTMs. Aggregation: Primarily FedAvg. Some use of FedProx, FedMA.
[25]	FL + Blockchain: Integration of FL for collaborative training and Blockchain for auditability, security, and access control.	IoMT/Wearables Data Analysis, Secure Medical Record Sharing, Disease Prediction.	FL for ML, Smart Contracts for access control and reward mechanisms, IPFS for off-chain data storage, various consensus protocols (PoW, PoA).
[17]	Blockchain + Multi-Agent Reinforcement Learning (MARL)	Resource Management in Healthcare Systems: Optimizing task allocation (e.g., patient requests to hospital resources) securely and efficiently.	Deep Q-Networks (DQN) for agent learning, PoA consensus on a permissioned blockchain (Hyperledger Besu).
[50]	FL + Blockchain	IoT-based Healthcare 4.0: Remote patient monitoring, real-time health data analysis from connected devices.	Not specified in detail, but describes a generic framework with layers for data acquisition, edge computing, FL, and blockchain.
[67]	FL	Primarily focused on the implementation process across various healthcare domains rather than a single use case.	Focuses on methods to address challenges: FedProx, Scaffold, FedNova for non-IID data; DP, Homomorphic Encryption for privacy.
[31]	Blockchain	Clinical Trials Management: Patient recruitment, informed consent, data integrity, results traceability, and supply chain for trial medications.	Smart Contracts for consent management, cryptographic hashing for data integrity, and decentralized identifiers (DIDs) for patient identity.
[70]	DAO: A framework for a civic data trust.	Civic Medical Data Management: Governing collective access to and use of patient data for research by stakeholders (patients, researchers, clinicians).	Smart contracts for policy enforcement; governance tokens for voting; data access protocols.
[61]	DAO + AI: Synergy between DAOs for funding and AI for project evaluation in DeSci.	Life Sciences R&D Financing: Creating decentralized ecosystems for funding early-stage biotech and pharmaceutical research.	Tokenization of intellectual property (IP-NFTs); AI for due diligence; smart contracts for funding distribution.
[68]	(General) DAO: A comprehensive review and proposed design framework for DAOs across sectors.	Not specific to healthcare. Principles are directly applicable to designing a healthcare DAO (e.g., for a hospital network or research consortium).	Tokenomics (incentive design), on-chain vs. off-chain voting mechanisms, smart contracts.
[47]	DAO: A detailed case study of the MakerDAO project.	Not healthcare-related (Domain: Decentralized Finance). Its relevance is as a real-world analogy for understanding mature DAO operations and challenges.	Ethereum smart contracts, MKR governance token, formal proposal, and on-chain voting systems.
[30]	(General) DAO: Architectural analysis of DAOs using enterprise frameworks.	Not specific to healthcare. A high-level analysis of DAO structures is useful for integrating them into large, existing organizations like hospital systems.	Enterprise Architecture (EA) modeling languages (ArchiMate), TOGAF framework.
[26]	DAO: Explores DAO governance for social impact and non-profit organizations.	Not specific to healthcare (Domain: Human Rights). Principles are directly applicable to non-profit healthcare organizations or patient advocacy groups.	Cardano blockchain, governance tokens, staking, and delegative voting mechanisms.
[53]	(General) DAO: A review of DAO applications in managing physical infrastructure.	Not specific to healthcare (Domain: Built Environments). Analogous to managing physical hospital infrastructure or a “smart hospital” campus.	Smart contracts for property management; IoT integration; tokenization of real estate assets.

Table 2. Final selection of publications analysis of architecture, use cases, and methods.

Author, Year	Architecture	Use Case(s)	Key Computational Methods
[66]	Primarily horizontal FL. Architectures include client-server (centralized coordinator) and, less commonly, peer-to-peer (decentralized).	Medical Imaging (brain tumor segmentation, diabetic retinopathy, chest X-ray analysis), EHR Data Analysis (mortality prediction, disease diagnosis).	Models: CNNs (U-Net, ResNet), LSTMs. Aggregation: Primarily FedAvg. Some use of FedProx, FedMA.
[25]	FL + Blockchain: Integration of FL for collaborative training and Blockchain for auditability, security, and access control.	IoMT/Wearables Data Analysis, Secure Medical Record Sharing, Disease Prediction.	FL for ML, Smart Contracts for access control and reward mechanisms, IPFS for off-chain data storage, various consensus protocols (PoW, PoA).
[17]	Blockchain + Multi-Agent Reinforcement Learning (MARL)	Resource Management in Healthcare Systems: Optimizing task allocation (e.g., patient requests to hospital resources) securely and efficiently.	Deep Q-Networks (DQN) for agent learning, PoA consensus on a permissioned blockchain (Hyperledger Besu).
[50]	FL + Blockchain	IoT-based Healthcare 4.0: Remote patient monitoring, real-time health data analysis from connected devices.	Not specified in detail, but describes a generic framework with layers for data acquisition, edge computing, FL, and blockchain.
[67]	FL	Primarily focused on the implementation process across various healthcare domains rather than a single use case.	Focuses on methods to address challenges: FedProx, Scaffold, FedNova for non-IID data; DP, Homomorphic Encryption for privacy.
[31]	Blockchain	Clinical Trials Management: Patient recruitment, informed consent, data integrity, results traceability, and supply chain for trial medications.	Smart Contracts for consent management, cryptographic hashing for data integrity, and decentralized identifiers (DIDs) for patient identity.
[70]	DAO: A framework for a civic data trust.	Civic Medical Data Management: Governing collective access to and use of patient data for research by stakeholders (patients, researchers, clinicians).	Smart contracts for policy enforcement; governance tokens for voting; data access protocols.
[61]	DAO + AI: Synergy between DAOs for funding and AI for project evaluation in DeSci.	Life Sciences R&D Financing: Creating decentralized ecosystems for funding early-stage biotech and pharmaceutical research.	Tokenization of intellectual property (IP-NFTs); AI for due diligence; smart contracts for funding distribution.
[68]	(General) DAO: A comprehensive review and proposed design framework for DAOs across sectors.	Not specific to healthcare. Principles are directly applicable to designing a healthcare DAO (e.g., for a hospital network or research consortium).	Tokenomics (incentive design), on-chain vs. off-chain voting mechanisms, smart contracts.
[47]	DAO: A detailed case study of the MakerDAO project.	Not healthcare-related (Domain: Decentralized Finance). Its relevance is as a real-world analogy for understanding mature DAO operations and challenges.	Ethereum smart contracts, MKR governance token, formal proposal, and on-chain voting systems.
[30]	(General) DAO: Architectural analysis of DAOs using enterprise frameworks.	Not specific to healthcare. A high-level analysis of DAO structures is useful for integrating them into large, existing organizations like hospital systems.	Enterprise Architecture (EA) modeling languages (ArchiMate), TOGAF framework.
[26]	DAO: Explores DAO governance for social impact and non-profit organizations.	Not specific to healthcare (Domain: Human Rights). Principles are directly applicable to non-profit healthcare organizations or patient advocacy groups.	Cardano blockchain, governance tokens, staking, and delegative voting mechanisms.
[53]	(General) DAO: A review of DAO applications in managing physical infrastructure.	Not specific to healthcare (Domain: Built Environments). Analogous to managing physical hospital infrastructure or a “smart hospital” campus.	Smart contracts for property management; IoT integration; tokenization of real estate assets.

The findings reveal a distinction in the maturity and focus of these technologies. FL has been validated in multi-institutional studies, proving its viability for collaborative research. In contrast, most Blockchain and nearly all DAO applications in healthcare remain at the conceptual or proof-of-concept stage. A significant portion of the literature on DAOs is not specific to healthcare, instead drawing analogies from real-world case studies or proposing general design frameworks. This indicates that while the technical application of dAI for analysis is advancing, the use of decentralized structures for complex governance and organizational management in healthcare is still a nascent and largely theoretical field.

Table 3 reveals a distinction between the challenges facing different decentralized technologies. For FL, the primary risks are technical and data-centric, including managing non-IID data, high communication overhead, and protecting against model-based privacy leaks. However, the implementation of blockchain technology in healthcare encounters substantial challenges, including infrastructural limitations and regulatory uncertainties. These challenges encompass aspects such as the inability to scale up to meet growing demands, the complexity of integrating with existing hospital systems, and navigating an evolving legal landscape, which includes the European Union’s GDPR and its “Right to be Forgotten” provisions. The most complex challenges for DAOs are those that span the socio-technical, legal, and governance domains. The most significant risks include governance capture by large token-holders, low member participation (voter apathy), the absence of a clear legal status, and vulnerabilities in the underlying smart contracts.

Table 3. Analysis of benefits, challenges, risks, and best practices.

Author, Year	Reported Benefits	Reported Challenges and Risks	Best Practices
[66]	Data Privacy: Enables model training without sharing raw patient data. Access to Diverse Data: Breaks down data silos, leading to more generalizable and robust models.	Statistical Heterogeneity (Non-IID data), high communication costs, system heterogeneity (hardware/software variance), vulnerability to model poisoning attacks.	Need for benchmarks on diverse, real-world datasets. Recommends privacy-enhancing techniques like Differential Privacy (DP) and Secure Multi-Party Computation (SMC).
[25]	Enhanced Security and Trust: Blockchain provides an immutable audit trail of model updates and data access. Incentivization: Smart contracts can automate rewards for data contributors.	Scalability issues of blockchain, high computational/energy cost of PoW and integration complexity between FL and blockchain systems.	Proposes a layered architecture (Perception, Edge, Blockchain-FL) as a reference model. Highlights the opportunity for tokenomics to encourage participation.
[17]	Decentralized Decision-Making: Agents learn optimal policies without a central controller. Security and Auditability: Blockchain records all actions and decisions transparently.	Simulation may not capture real-world complexity. High setup complexity for MARL systems.	Demonstrates the potential of agentic AI for optimizing dynamic healthcare operations, moving beyond just data analysis.
[50]	Improved data security for sensitive IoT data, enhanced privacy for remote monitoring, and created a trusted ecosystem for data sharing.	Interoperability of diverse IoT devices, data quality from consumer-grade sensors, and managing network latency.	Provides a high-level architectural blueprint for integrating these technologies in an IoT context.
[67]	Privacy preservation is the primary benefit. Also cites improved fairness and reduced bias by including data from diverse populations.	“Data-centric” challenges (non-IID, missing data) and “Model-centric” challenges (communication bottlenecks, security). Highlights “pitfalls” like data leakage risks.	Recommends a “checklist” for FL implementation: Define the clinical problem, characterize data, select appropriate FL algorithms, and plan for ethical review.
[31]	Enhanced Data Integrity and Transparency: Immutable record of trial data and protocol adherence. Patient Empowerment: Patients can control access to their data via smart contracts.	Interoperability with existing hospital EMR systems, regulatory uncertainty (e.g., FDA/EMA acceptance), and data privacy concerns if not designed correctly (Right to be Forgotten).	Highlights the potential to reduce fraud and improve reproducibility in clinical research. Emphasizes starting with non-critical applications like consent tracking.
[70]	Democratic Control: Gives citizens a direct voice in how their data is used. Transparency: All governance decisions are on a public ledger.	Legal and Regulatory Uncertainty: DAOs lack a clear legal status. Scalability of Governance: Ensuring efficient decision-making with many participants.	Provides a blueprint for establishing a “Data DAO,” emphasizing the need for a clear constitution encoded into smart contracts.
[61]	Access to Capital: Opens new, global funding avenues. Community Ownership: Allows patients/researchers to own a stake in the research they support.	Valuation of IP: Difficulty in accurately valuing early-stage research. Regulatory Compliance (securities law).	Highlights the potential for DAOs to create more equitable R&D ecosystems, especially for rare or less commercially viable diseases.
[68]	Automation of Governance: Reduces administrative overhead. Censorship Resistance: Decisions cannot be easily overturned by a central party.	Plutocracy: Risk of governance being dominated by large token holders. Smart Contract Vulnerabilities.	Offers a structured methodology for any DAO design: define purpose, design tokenomics, then structure governance.
[47]	Resilience and Decentralization: Has operated for years without central control, allowing community engagement.	Centralization Risks from early token distributions. High Complexity for new users. Regulatory Scrutiny.	Provides critical real-world lessons: the need for both on-chain voting and off-chain discussion forums, and dedicated teams for risk assessment.
[30]	Provides a structured language for describing DAOs, making them more understandable to enterprise architects and IT managers.	Highlights a cultural and methodological gap between formal enterprise structures and the fluid nature of DAOs.	Useful for integrating DAOs into existing enterprises, providing tools to bridge traditional IT governance with decentralized models.
[26]	Global and Inclusive Governance: Allows members worldwide to participate. Transparent Funding: Donors can track fund usage on-chain.	Member Apathy: Ensuring sustained participation from a non-financially motivated membership base. Technical Barriers for users.	Provides a governance model well-suited for mission-driven organizations where stakeholder alignment is paramount.
[53]	Transparent and Efficient Management: Automating tasks like maintenance requests and access control. Shared Ownership of physical assets.	Legal Integration: Connecting on-chain ownership to real-world legal titles. IoT Security: Ensuring the integrity of sensor data.	Explores how DAOs can move from governing digital systems to governing complex physical infrastructure.

Table 3. Analysis of benefits, challenges, risks, and best practices.

Author, Year	Reported Benefits	Reported Challenges and Risks	Best Practices
[66]	Data Privacy: Enables model training without sharing raw patient data. Access to Diverse Data: Breaks down data silos, leading to more generalizable and robust models.	Statistical Heterogeneity (Non-IID data), high communication costs, system heterogeneity (hardware/software variance), vulnerability to model poisoning attacks.	Need for benchmarks on diverse, real-world datasets. Recommends privacy-enhancing techniques like Differential Privacy (DP) and Secure Multi-Party Computation (SMC).
[25]	Enhanced Security and Trust: Blockchain provides an immutable audit trail of model updates and data access. Incentivization: Smart contracts can automate rewards for data contributors.	Scalability issues of blockchain, high computational/energy cost of PoW and integration complexity between FL and blockchain systems.	Proposes a layered architecture (Perception, Edge, Blockchain-FL) as a reference model. Highlights the opportunity for tokenomics to encourage participation.
[17]	Decentralized Decision-Making: Agents learn optimal policies without a central controller. Security and Auditability: Blockchain records all actions and decisions transparently.	Simulation may not capture real-world complexity. High setup complexity for MARL systems.	Demonstrates the potential of agentic AI for optimizing dynamic healthcare operations, moving beyond just data analysis.
[50]	Improved data security for sensitive IoT data, enhanced privacy for remote monitoring, and created a trusted ecosystem for data sharing.	Interoperability of diverse IoT devices, data quality from consumer-grade sensors, and managing network latency.	Provides a high-level architectural blueprint for integrating these technologies in an IoT context.
[67]	Privacy preservation is the primary benefit. Also cites improved fairness and reduced bias by including data from diverse populations.	“Data-centric” challenges (non-IID, missing data) and “Model-centric” challenges (communication bottlenecks, security). Highlights “pitfalls” like data leakage risks.	Recommends a “checklist” for FL implementation: Define the clinical problem, characterize data, select appropriate FL algorithms, and plan for ethical review.
[31]	Enhanced Data Integrity and Transparency: Immutable record of trial data and protocol adherence. Patient Empowerment: Patients can control access to their data via smart contracts.	Interoperability with existing hospital EMR systems, regulatory uncertainty (e.g., FDA/EMA acceptance), and data privacy concerns if not designed correctly (Right to be Forgotten).	Highlights the potential to reduce fraud and improve reproducibility in clinical research. Emphasizes starting with non-critical applications like consent tracking.
[70]	Democratic Control: Gives citizens a direct voice in how their data is used. Transparency: All governance decisions are on a public ledger.	Legal and Regulatory Uncertainty: DAOs lack a clear legal status. Scalability of Governance: Ensuring efficient decision-making with many participants.	Provides a blueprint for establishing a “Data DAO,” emphasizing the need for a clear constitution encoded into smart contracts.
[61]	Access to Capital: Opens new, global funding avenues. Community Ownership: Allows patients/researchers to own a stake in the research they support.	Valuation of IP: Difficulty in accurately valuing early-stage research. Regulatory Compliance (securities law).	Highlights the potential for DAOs to create more equitable R&D ecosystems, especially for rare or less commercially viable diseases.
[68]	Automation of Governance: Reduces administrative overhead. Censorship Resistance: Decisions cannot be easily overturned by a central party.	Plutocracy: Risk of governance being dominated by large token holders. Smart Contract Vulnerabilities.	Offers a structured methodology for any DAO design: define purpose, design tokenomics, then structure governance.
[47]	Resilience and Decentralization: Has operated for years without central control, allowing community engagement.	Centralization Risks from early token distributions. High Complexity for new users. Regulatory Scrutiny.	Provides critical real-world lessons: the need for both on-chain voting and off-chain discussion forums, and dedicated teams for risk assessment.
[30]	Provides a structured language for describing DAOs, making them more understandable to enterprise architects and IT managers.	Highlights a cultural and methodological gap between formal enterprise structures and the fluid nature of DAOs.	Useful for integrating DAOs into existing enterprises, providing tools to bridge traditional IT governance with decentralized models.
[26]	Global and Inclusive Governance: Allows members worldwide to participate. Transparent Funding: Donors can track fund usage on-chain.	Member Apathy: Ensuring sustained participation from a non-financially motivated membership base. Technical Barriers for users.	Provides a governance model well-suited for mission-driven organizations where stakeholder alignment is paramount.
[53]	Transparent and Efficient Management: Automating tasks like maintenance requests and access control. Shared Ownership of physical assets.	Legal Integration: Connecting on-chain ownership to real-world legal titles. IoT Security: Ensuring the integrity of sensor data.	Explores how DAOs can move from governing digital systems to governing complex physical infrastructure.

The literature delineates a series of evolving best practices and governance models. In the context of FL, the prevailing best practices emphasize the utilization of a formal implementation checklist and the integration of privacy-enhancing technologies, such as Differential Privacy. The governance of these technologies is typically assumed by a consortium of participating institutions. For DAOs, a more diverse array of governance structures is proposed, transcending the limitations of rudimentary token voting systems. These structures encompass Delegative Democracy (liquid democracy) and hybrid models that integrate community participation with the oversight of expert review councils. A fundamental insight that emerges is the paramount necessity for a well-defined “constitution,” whether it manifests as a legal agreement within a consortium or as rules encoded in smart contracts for a DAO. This constitution must be supported by both formal on-chain processes and robust off-chain community discussion to ensure the effective and equitable operation of these systems.

5. Discussion

5.1. Results: Architectural Specialization and the “Concept-to-Practice” Gap

The results of this systematic review reveal that the dAI ecosystem in healthcare is at a critical inflection point, moving from theoretical postulation toward pragmatic implementation. The analysis provides a clear answer to our research questions by mapping the field’s primary architectures, use cases, and maturity levels. The most significant finding is the emergence of a sophisticated “division of labor” among dAI technologies, where a specialized toolkit is being developed to solve distinct, multifaceted challenges rather than a single, one-size-fits-all solution. In addressing our first and second research questions, our findings confirm that FL, Blockchain, and DAOs serve different but complementary functions. FL has clearly emerged as the vanguard architecture for privacy-preserving clinical. Data collection and Computation. Its consistent application in data-intensive domains like medical imaging and EHR analysis positions it as the primary solution for breaking down data silos without compromising patient privacy. The bibliometric analysis substantiates this, placing FL at the nexus of key concepts like “interoperability” and “explainable AI”. While it is the most mature dAI modality, its path to production is moderated by technical hurdles like non-IID data, which the research community is actively addressing with advanced algorithms.

Conversely, Blockchain is primarily being leveraged as an infrastructural remedy for the “trust deficit” in multi-stakeholder health data exchange. Its function is to provide a tamper-proof ledger where auditability and integrity are paramount, finding its best fit in operational use cases like enhancing the integrity of clinical trials and securing patient records. This role is confirmed by the keyword maps, which tightly cluster “blockchain” with terms like “security,” “challenge,” and “digital identity challenge”. Finally, DAOs represent the most transformative, albeit nascent, architecture, a sociotechnical framework for reimagining governance and collaboration. As our network analysis of governance terms illustrates, the conversation is shifting from traditional technology “integration” to creating entirely new organizational models. Applications in governing civic data trusts or funding DeSci initiatives aim to democratize access to data and research funding. However, the profoundly conceptual nature of DAO research underscores that the legal and socioeconomic frameworks required for functional healthcare DAOs are still in their infancy.

The preponderance of proofs-of-concept across all architectures reveals a significant concept-to-practice gap. The implication is that while the value proposition of dAI is widely accepted, the evidence base for its real-world clinical and economic impact is yet to be established as a critical avenue for future research. The core benefits that are consistently reported (privacy, trust, data sovereignty, security) are the fundamental drivers of the entire field. This suggests that dAI is not a technology seeking to address a specific problem; rather, it is a direct response to the inherent limitations and ethical conflicts of centralized AI in the highly regulated and sensitive healthcare environment. The impetus for this research stems from the imperative to ensure that the capabilities of AI are aligned with the non-negotiable principles of patient confidentiality and trust. Conversely, the aforementioned challenges (scalability, interoperability, and governance complexity) represent the significant practical barriers to adoption. The issue of interoperability is of particular concern. The findings suggest that the absence of clear strategies for integrating with legacy hospital systems (e.g., via FHIR standards) results in the isolation of even the most advanced dAI solutions, hindering their ability to achieve a systemic impact. In a similar vein, the intricate nature of governance in DAOs underscores the necessity of not only technical proficiency but also a nuanced understanding of economics (tokenomics), law, and social organization. This underscores the profoundly interdisciplinary character of the endeavor [21,28,70].

Finally, the findings indicate that the literature is commencing the process of distilling actionable frameworks and guidelines. The fundamental implication of this observation is that, while mature, standardized best practices are not yet in existence, a set of guiding principles is beginning to emerge. The growing emphasis on hybrid architectures, the necessity of defining governance rules in advance, and the strategic value of starting with well-defined use cases reflect a growing pragmatism. A particularly salient actionable insight is the necessity of interdisciplinary learning, particularly for DAOs. The literature’s reliance on case studies from Decentralized Finance and other non-healthcare domains implies that healthcare leaders cannot develop these new models in a vacuum. To make progress, it is necessary to consider the successes and failures of existing decentralized systems from an outside perspective. The prevailing notion asserts that the successful implementation of dAI will be equally influenced by organizational and strategic innovation as it is by technological expertise.

5.2. Core Drivers, Restraining Forces, and Inherent Tensions

The challenge of interoperability stands out as a critical barrier; without seamless integration with legacy hospital IT systems via standards like FHIR, even the most advanced dAI solutions risk becoming isolated research projects rather than system-wide transformations. Furthermore, a deeper analysis reveals inherent tensions within the ecosystem that represent strategic trade-offs:

• Transparency vs. Privacy: The immutable and often public nature of blockchain ledgers, designed for transparency, is in direct conflict with the principle of data minimization and the “right to be forgotten” enshrined in regulations like GDPR. Architectures that combine these technologies must carefully manage what data is stored on-chain versus off-chain to reconcile this tension.
• Decentralized Governance vs. Clinical Accountability: The democratic ideal of a DAO, where decisions are made by a distributed network of token-holders, poses a challenge to traditional models of clinical governance and legal accountability. In a high-stakes medical environment, establishing clear lines of responsibility for an AI model’s recommendation is non-negotiable, a requirement that purely decentralized systems struggle to meet without hybrid models incorporating expert oversight.

This specialization is not an arbitrary choice; rather, it is a direct response to the multifaceted nature of the problems within the healthcare domain, which span computational, administrative, and governance challenges. FL has emerged as the vanguard architecture for privacy-preserving clinical computation. Its consistent application in data-intensive domains such as medical imaging and EHR analysis positions it as the primary solution to the clinical data access problem, allowing institutions to collaborate without compromising patient privacy. Also, the bibliometric network analyses confirm its centrality, placing it at the nexus of Interoperability, Edge Computing, and Explainable AI. While it is the most mature dAI modality, with some multi-institutional pilots underway, its progression is moderated by persistent technical hurdles, most notably the statistical heterogeneity of non-IID data. Conversely, blockchain is regarded as a potential remedy for the “trust deficit” that is inherent to health data exchange involving multiple stakeholders. The primary function of this technology is to serve as an infrastructure, providing a tamper-proof ledger for transactions where auditability and integrity are paramount. As indicated in the literature, the optimal utilization of the system is within the operational and administrative domain, with a particular emphasis on the enhancement of the integrity of Clinical Trials Management. The keyword maps further substantiate this by tightly clustering Blockchain with terms such as Security, Challenge, and the Digital Identity Challenge, thereby confirming its role as a trust-enabling framework.

5.3. Actionable Frameworks and the Interdisciplinary Mandate

The substantial research impact observed from the United States and the mounting interest across Europe are indicative of this phenomenon, as these regions operate under stringent regulatory frameworks that render dAI’s value proposition particularly compelling. Conversely, the challenges that have been reported with consistency function as potent restraining forces. The issue of interoperability emerged as a particularly critical barrier. As indicated by its central position in network maps, the successful adoption of dAI systems is contingent upon their ability to seamlessly communicate with the existing IT infrastructure of healthcare, including EHRs and PACS, employing standards such as FHIR. If this issue is not addressed, there is a risk that dAI will devolve into a series of disconnected, albeit impactful, research initiatives. Conversely, the governance complexity of DAOs stands as a formidable challenge, arguably surpassing other significant obstacles. The findings indicate that the creation of a viable healthcare DAO is not merely a coding problem; rather, it is a complex design challenge requiring an interdisciplinary fusion of expertise in economics (tokenomics), law, ethics, and organizational behavior.

In addressing our fourth objective, our findings indicate that the literature is beginning to distill actionable “blueprints” rather than mature, standardized best practices. The most critical insight for practitioners and policymakers is the interdisciplinary mandate required for success. Progress in this field is as dependent on innovation in organizational strategy, law, and economics as it is on technical expertise. The necessity of drawing governance lessons from DeFi or applying enterprise architecture frameworks to DAO design proves that healthcare leaders cannot develop these models in a vacuum. The pragmatic path forward is to start with well-defined problems where decentralization offers an undeniable advantage, demonstrate value on a smaller scale, and then iteratively address the complex challenges of integration and governance.

6. Conclusions

6.1. Findings

The contemporary research landscape is marked by accelerated progress and substantial innovation, yet also by a notable gap between conceptual frameworks and their practical application in clinical settings. The analysis corroborates the hypothesis that decentralized AI is not a monolithic entity but a specialized toolbox of architectures, each tailored to address distinct, fundamental challenges in healthcare. A key finding is that decentralization is best understood as a continuum rather than an absolute state, hybrid approaches may prove optimal in many contexts. A central body may still define global model objectives while training and data processing are increasingly distributed. Federated learning and blockchain have reached meaningful proof-of-concept maturity, yet the field remains largely pre-clinical, dominated by simulations and pilot implementations rather than large-scale longitudinal deployments.

The primary contribution of this review is a structured, evidence-based synthesis of a complex and rapidly evolving domain. By categorizing core technologies, mapping clinical use cases, and interpreting overarching trends, this study offers a useful baseline across stakeholder groups. For researchers, it elucidates the intellectual clusters driving the field and identifies its most pressing gaps. For clinicians and healthcare leaders, it grounds the potential of these technologies in the reality of their current maturity. For policymakers, it underscores the urgency of establishing clear regulatory frameworks that can encourage responsible innovation. Future research should prioritize longitudinal, real-world studies measuring tangible clinical impact, patient outcomes, and economic viability of dAI systems in active healthcare environments. Interoperability challenges must be addressed through robust standardization frameworks aligned with HL7/FHIR standards. Governance models for decentralized systems, particularly DAOs, require rigorous interdisciplinary development spanning law, ethics, and incentive design. Despite persistent challenges, decentralized AI represents a genuine paradigm shift, one whose transition from conceptual promise to transformative reality depends on sustained collaboration between researchers, practitioners, and policymakers.

6.2. Limitations

As with any systematic review, this study is subject to a number of inherent methodological constraints worth acknowledging. The findings are based on published academic literature, which tends to favor positive results; underreporting of negative outcomes or failed implementations is a known characteristic of technology-oriented research, and this review is unlikely to be an exception. Additionally, the rapid pace of innovation in this domain means the review captures a snapshot in time, and relevant advancements may have emerged since the literature search concluded. The scope of the database search may have missed contributions from engineering-focused repositories such as IEEE Xplore or ACM Digital Library. The English-language restriction similarly limits coverage, particularly from active research communities in China and other non-English-speaking regions. Finally, the purposive-stratified selection of 13 studies for in-depth analysis, while methodologically justified, may have prioritized well-documented implementations over equally informative but less prominently published work. These limitations are acknowledged transparently and do not undermine the overall validity of the synthesis, but should be considered when interpreting specific findings.