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Review

Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients

by
Emilia Mikołajewska
1,*,
Urszula Rogalla-Ładniak
2,
Jolanta Masiak
3,
Ewelina Panas
4 and
Dariusz Mikołajewski
5
1
Faculty of Health Sciences, Ludwik Rydygier Collegium Medicum, Nicolaus Copernicus University, 85-067 Bydgoszcz, Poland
2
Faculty of Medicine, Ludwik Rydygier Collegium Medicum, Nicolaus Copernicus University, 85-067 Bydgoszcz, Poland
3
Faculty of Medicine, Medical University of Lublin, 20-059 Lublin, Poland
4
Higher Education Internationalisation Laboratory, Institute of International Relations, Faculty of Political Science and Journalism, Maria Curie-Skłodowska University, 20-031 Lublin, Poland
5
Faculty of Computer Science, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(1), 318; https://doi.org/10.3390/app16010318
Submission received: 8 December 2025 / Revised: 23 December 2025 / Accepted: 26 December 2025 / Published: 28 December 2025
(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)
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Featured Application

The potential application of the article includes AI-based DTs in healthcare.

Abstract

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy.

1. Introduction

Cyber–physical systems (CPS) in healthcare arose from decades of medical research focused on continuous, real-time monitoring of physiological signals [1]. Early biomedical engineering studies demonstrated that integrating sensors with computational models could improve diagnosis and personalize treatment. Social research highlighted the growing need for patient-centered care, especially for older adults and those with chronic conditions. This pressure motivated a shift from episodic clinical assessments to continuous, data-driven health assessments [2]. Advances in artificial intelligence (AI) enabled the transformation of raw sensor data into valuable clinical insights through predictive modeling [3]. Patient digital twins (DTs) emerged as virtual representations reflecting biological, behavioral, and environmental conditions [4]. Their development required combining clinical evidence with social research on lifestyle, medication adherence, and patient behavior [5]. CPSs use these DTs to simulate disease progression, treatment response, and potential risks before intervention [6]. This feedback loop between physical sensors and virtual models has created a new paradigm for adaptive, personalized healthcare [6]. AI-enhanced DTs are the foundation of next-generation healthcare systems, enabling proactive, safe, and socially responsive medical systems, similar to predictive maintenance in industry [7].
CPSs in healthcare are currently introducing an unprecedented integration of a patient’s physiology, behavior, and environment in real time. Their key innovation lies in tightly coupling sensor data with AI-based DTs, which are continuously updated as the patient’s condition changes [8]. Medical research provides advanced physiological models that allow these DTs to mimic organ function and disease dynamics with increasing accuracy [9]. Social sciences provide insights into lifestyle, medication adherence, and psychosocial factors, enabling CPSs to represent not only biological states but also real-world human contexts [10]. These systems provide clinicians with personalized simulations that predict treatment outcomes before decisions are made [11]. CPSs also improve patient safety by early detection of anomalies and triggering automated, context-aware interventions [12]. They support personalized rehabilitation by adapting care pathways to the patient’s daily activities and social environment [13]. In population health, aggregated DT data reveal patterns that influence public health planning and resource allocation [14]. Their integration improves communication between patients and physicians through transparent, continuously updated health profiles. The current contribution of CPS with AI-based DTsi represents a shift toward predictive, holistic, and deeply personalized health [15].
Current research gaps in healthcare regarding CPS using AI-based DTs include limited clinical validation of complex physiological models in diverse patient populations. Many DTs still struggle to accurately represent multimorbidity, despite its prevalence in real-world medical settings [16]. Sensor data quality and interoperability remain inconsistent, creating gaps in the reliability of real-time CPS feedback loops [17]. Social and behavioral factors, while recognized as important, are often modeled superficially due to insufficient longitudinal studies [18]. A standardized framework integrating social determinants of health into DTs architecture is also lacking. Ethical issues (particularly privacy, autonomy, and algorithmic bias) remain unresolved and hinder large-scale implementation [19]. Current AI models in DTs often function as “black boxes”, limiting clinical trust and interpretability [20]. Ensuring the security of CPS is challenging because adaptive, learning systems behave unpredictably in new environments. Integrating CPS into existing healthcare processes remains inefficient, and usability barriers exist for both medical professionals and patients [21]. Obtaining large, high-quality datasets that combine biomedical, behavioral, and environmental data is difficult, limiting the scientific maturity of personalized DTs [22].
CPSs in healthcare represent a deep integration of computational intelligence, physical medical devices, and patient-centric data, enabling continuous, adaptive, and personalized care. They combine real-time measurements, AI-based analytics, and networked medical devices to monitor, predict, and optimize patient health. This allows these systems to dynamically respond to changing patient needs and support medical staff in making more informed clinical decisions. They also enable better care coordination by integrating data from multiple sources and ensuring their secure exchange. As a result, CPSs contribute to improved treatment quality, increased treatment effectiveness, and reduced risk of medical errors [23].
The aim of this study is to systematically review how healthcare CPSs integrate medical and social research to support the development of AI-based patient DTs. The study aims to identify scientific advances, challenges, and interdisciplinary contributions that enable accurate representation of patient health and behavior in real time. Ultimately, the study aims to explain how these integrated systems can improve personalized care, clinical decision-making, and population-level health strategies. We posed four research questions (RQs):
  • RQ1: How do current cyber–physical systems integrate physiological, behavioral, and environmental data to create AI-based patient DTs?
  • RQ2: What advances in medical and social research play the most important role in increasing the accuracy, personalization, and clinical relevance of patient diagnoses?
  • RQ3: What technical, ethical, and methodological gaps (including in the areas of well-being and user eXperience (UX)) limit the effective implementation of cyber–physical systems with AI-based DTs in real-world healthcare settings?
  • RQ4: How can interdisciplinary evidence be used to improve the design, validation, and scalability of cyber–physical systems that support patient-centered, predictive, and proactive healthcare?

2. Materials and Methods

2.1. Dataset

This bibliometric analysis sought to assess the current body of knowledge and practical approaches to planning and implementing AI-based digital twin (DT) development strategies within healthcare systems (CPSs), with the goal of better leveraging existing knowledge and experience for both present and future healthcare contexts. Analyses that focus on a single patient or a uniform patient group often fail to capture the broader potential of these solutions, particularly when viewed through an interdisciplinary lens. Accordingly, we applied bibliometric techniques to examine recent global scientific publications from the past decade (i.e., from January 2016 to publications already dated 2026 on 30 November 2025).
The timeframe was intentionally limited to recent years to capture the rapid methodological and application advances in CPS and AI-based DTs that have emerged with increasing computing power, data availability, and clinical digitization. Newer research was prioritized because the integration of AI, real-time sensors, and patient-specific modeling has only recently reached a level of maturity suitable for healthcare implementation and evaluation. This approach ensures that the bibliometric analysis reflects the current state of knowledge, including clinically relevant use cases, validation practices, and ethical considerations that were largely absent from earlier foundational work. Although the core concepts of CPS and DTs stem from earlier engineering and systems theory literature, these works primarily address generic architectures rather than healthcare-specific, patient-centric implementations. Earlier foundational research, therefore, shapes the conceptual framework but does not adequately reflect the interdisciplinary intersection of medical, social, and AI research that defines contemporary patient DTs. As a result, the chosen time frame balances conceptual continuity with practical relevance, allowing for a more accurate representation of current research trends and translational impact on CPS-based healthcare DTs.
Criteria for inclusion of articles in the review were following: English-language original and review articles, also full-text conference articles and chapters in books, indexed by major bibliometric databases: WoS, Scopus, PubMed, dblp. Criteria for exclusion from the review were following: languages other than English, other forms of publication (reports, abstracts, etc.).
We focused on the most common publication sources, leading topics, countries, institutions, researchers, and selected Sustainable Development Goals (SDGs) to map the global landscape in the study area. These dimensions reveal where scientific knowledge and innovation are concentrated, helping to identify geographic and institutional factors influencing technological progress and clinical implementation. They also enable us to examine how emerging topics (including UX in AI-based DTs) and align with the wider agenda of sustainability and health-related SDGs, highlighting their societal significance. Collectively, these research questions establish a structured framework that clarifies not only the topics being investigated within the field, but also the stakeholders driving the work and the underlying motivations, thereby offering strategic guidance for future research and development efforts.
The proposed approach and research questions enable a more holistic understanding of prevailing trends, strategies, and practices in both research and clinical settings concerning the planning, implementation, and application of AI-driven digital transformation technologies. Gaining such insight is crucial for identifying and organizing the next necessary steps, integrating existing solutions, and maximizing their potential, for example, through the development of a structured action plan. Consequently, the interpretation of bibliometric data will deepen ongoing discussions and establish a robust basis for future research, scholarly output, and the advancement of clinical practice, particularly in light of emerging demographic challenges.

2.2. Methods

The bibliometric analysis procedure follows a structured process, aligned with the key elements of PRISMA 2020, to ensure transparency and reproducibility. First, a clear research question and scope are defined, specifying the field, time period, and publication types to be analyzed. Second, numerous scientific databases are systematically searched using predefined keywords and Boolean logic, and the search strategy is documented. Third, records are screened using explicit inclusion and exclusion criteria, and duplicates are removed to ensure consistency within the dataset. Fourth, eligibility is assessed by analyzing titles, abstracts, and full texts, in accordance with PRISMA study selection guidelines. Fifth, metadata such as authors, affiliations, keywords, citations, and abstracts are extracted and cleaned for analysis. Sixth, bibliometric metrics (e.g., publication trends, citation impact, co-authorship, and co-word networks) are calculated using recognized tools. Seventh, thematic mapping and clustering techniques are used to identify research frontiers and emerging topics. Eighth, results are verified through sensitivity checks, cross-database comparisons, or expert review to reduce bias. Ninth, PRISMA-style flowcharts and tables are used to report the selection process and characterization of the dataset.The novelty of this “in-house approach” lies not in inventing new bibliometric steps but in the explicit adaptation and integration of selected PRISMA 2020 elements into the bibliometric analysis—thus explaining the rigor of reporting and methodological transparency in an area where PRISMA is not traditionally standardized.
This study conducted searches across four leading bibliographic databases—Web of Science (WoS), Scopus, PubMed, and dblp. These sources were chosen because they index a broad spectrum of publications spanning both technical and medical fields and provide comprehensive metadata with strong global coverage and relevance (Table 1). To ensure relevance, filters were applied to restrict results to English-language articles. Following this screening, each retrieved article was independently and manually assessed by three reviewers based on the predefined inclusion criteria, and the final sample size was determined by a two-out-of-three majority vote. The dataset was subsequently examined to identify its key characteristics, such as the most prolific authors, their associated research groups or institutions, countries of origin, subject areas, and emerging trends. This analysis made it possible to map core terminology and its development over time, along with the most significant advances in the field. Where feasible, temporal patterns were analyzed to observe shifts in research focus, and publications were organized into thematic clusters that revealed connections across different domains of study. Through this process, prominent themes and emerging subfields were identified, while the interdisciplinary composition of the author team supported a comprehensive evaluation and interpretation of the review outcomes.
Duplicate records were identified by exporting all retrieved references from each database into a unified bibliographic management platform (Biblioshiny), where entries with matching titles, DOIs, author lists, or publication years were automatically detected. Further automated checks examined additional metadata fields, including journal title, volume, issue, and page numbers, to capture instances in which titles were similar but not exactly the same. Following the automated procedures, manual verification was performed on borderline cases—such as conference papers and their extended journal versions—to ensure accurate classification. Together, these measures ensured the removal of all duplicate or near-duplicate records prior to screening, thereby maintaining the integrity of the dataset.
The study utilized 10 selected elements of the PRISMA 2020 bibliographic review guidelines, focusing on the following aspects: rationale (item 3), objectives (item 4), eligibility criteria (item 5), information sources (item 6), search strategy (item 7), selection process (item 8), data collection process (item 9), synthesis methods (item 13a), synthesis results (item 20b), and discussion (item 23a) (PRISMA 2020 partial checklist in Supplementary Materials).
A review may be preferable to a systematic review in the area of CPS in healthcare because AI-based patient DTs integrate heterogeneous medical, technical, and social research, which is often difficult to standardize. Systematic reviews require rigid inclusion criteria that may exclude new concepts, interdisciplinary insights, and qualitative social dimensions crucial to understanding patient-centered digital twins.In contrast, a simple review allows for a reflective synthesis of clinical data, ethical considerations, human-AI interactions, and the sociotechnical contexts that shape actual implementation. CPSs evolve rapidly, and a narrative approach can more flexibly incorporate state-of-the-art AI methods, prototypes, and conceptual frameworks before sufficient empirical research is available. Simple reviews better support the theory-building and contextual interpretation necessary to align DTs with patient experiences, clinical judgment, and societal impact.
The selected review methodology supports research replication by enabling precise classification by concepts, research areas, authors, documents, and sources. Results are presented in tabular form, allowing for further, flexible analysis and visualization.
Biblioshiny (open source) functioned as an analytical intermediary for examining, processing, cleaning, and harmonizing bibliographic records obtained from the four selected databases. It facilitated descriptive bibliometric analyses, such as yearly publication trends, leading authors, high-impact journals, and keyword co-occurrence networks. The resulting outputs supported the synthesis of findings by uncovering structural patterns within the research domain that may not have been evident through qualitative analysis alone.

3. Results

3.1. Data Sources

To narrow the scope of the search, advanced filters were applied to restrict the results to English-language publications. The search strategy was implemented as follows:
  • In Web of Science (WoS), the “Subject” field was used, encompassing the title, abstract, keywords, and related terms;
  • In Scopus, searches were conducted across the article title, abstract, and keywords;
  • In PubMed and dblp, customized manual keyword sets were employed.
Across all databases, articles were retrieved based on predefined keyword combinations: healthcare AND (“cyber-physical” OR “cyber physical”) AND “digital twin” AND (“artificial intelligence” OR “machine learning”) (Table 2).
The selected publication set was then further refined by manually selecting articles, removing irrelevant publications and duplicates to determine the final sample size (Figure 1).
The aforementioned PRISMA 2020 flowchart provides a clear, step-by-step overview of the process of identifying, selecting, assessing, and ultimately including studies in the review, ensuring methodological accuracy and reproducibility. It begins with the identification phase, which involves collecting data from databases and other sources, capturing the total volume of potentially relevant literature before removing duplicates. Next, in the screening phase, titles and abstracts are filtered based on predefined eligibility criteria, eliminating studies that are clearly not related to machine learning-based wearable devices or digital transformation technologies in rehabilitation. Next, in the eligibility phase, full-text articles are thoroughly assessed for methodological quality, relevance to data collection and security, and adequacy. Finally, the study inclusion phase shows how many articles remain after exclusion, representing the evidence base used for synthesis and analysis. Interpreting the PRISMA diagram allows for a better understanding of the transparency of the review, showing not only the volume of literature analyzed but also the rationale for each exclusion step, which strengthens the validity of the conclusions.

3.2. General Results of Analysis

An overview of the bibliometric analysis findings is provided in Table 3 and Figure 2, Figure 3, Figure 4 and Figure 5. In total, 47 articles published over the past decade (from January 2016 to November 2025, including journal issues dated 2026) were included in the analysis. Publications from the period 2016–2020 were excluded because they did not contain sufficient relevant content (in the years 2016–2020, no publications meeting the inclusion criteria were observed)., while studies published prior to 2016 were omitted due to the rapid obsolescence of earlier knowledge in this fast-evolving field.
The implications of these bibliometric findings suggest that the high percentage of articles and book chapters and the low percentage of conference reviews may indicate a field emerging from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context.
Few publications included in the review identified SDGs, and this was not a criterion for inclusion. Reporting SDGs is not required in some countries/research institutions. However, including articles with and without SDGs allows for the review to incorporate diverse perspectives, including the role of SDGs and any constraints imposed, voluntarily or unintentionally, by researchers/countries/institutions within the framework of sustainable development.
A key achievement in the field of CPS is the emergence of patient data models (DTs), virtual models that reflect the individual biological, behavioral, and social characteristics of each person [23,24]. Using advanced AI algorithms, DTs can analyze complex sets of medical and environmental data, including genetics, lifestyle, and environmental conditions (Figure 6) [25]. These models enable simulation of the body’s response to various therapies, supporting more precise diagnostics and treatment planning tailored to the patient’s needs [26].
Furthermore, DTs can predict potential health risks before symptoms appear, enabling preventative measures [27]. As a result, they constitute a groundbreaking tool for personalized and predictive medicine, as well as preventive medicine for healthy individuals (Figure 7) [28].
Interdisciplinary evidence is fundamental to creating accurate, physiologically based, patient-specific DTs within healthcare CPS. Clinical trials provide validated disease models, biomarkers, treatment guidelines, and outcome data that ground DT in real-world human physiology and evidence-based medicine. Biomedical and systems engineering provide mechanistic models (e.g., cardiovascular, metabolic, or respiratory dynamics) and control theory that enable the DT to simulate physical processes and responses to interventions. AI and data science integrate these mechanistic models with machine learning methods trained on large clinical datasets to personalize prognoses for individual patients. Medical imaging, wearable sensors, and implantable devices provide real-time physiological measurements, ensuring that the DT continuously reflects the patient’s current state. Social and behavioral research provides contextual evidence, such as lifestyle, adherence to recommendations, socioeconomic status, stress, and exposure to environmental factors, which significantly influence the health process.
CPSs enable predictive healthcare, where potential complications can be detected and mitigated before the first symptoms appear. By analyzing trends and patterns in health data, these systems can detect abnormalities early and suggest preventative measures [29]. Furthermore, they support personalized therapy, dynamically adapting treatment based on real-time information from wearable devices and electronic medical records [30]. This adaptive approach allows for better response to changing patient conditions and improves the effectiveness of medical interventions. This leads to more responsive, precise, and effective healthcare (Figure 8).
CPS systems integrate computational algorithms, network communications, and physical processes to monitor and control real-world systems. Common types of CPS systems include smart medical devices, hospital automation systems, wearable health monitors, robotic surgery platforms, and intelligent diagnostic equipment. In healthcare, CPS systems combine sensors on or within the patient’s body, clinical information systems, and actuators or decision support tools to enable continuous and adaptive care. A CPS system is a dynamic, virtual representation of a physical entity, such as a patient, that is continuously updated with real-time and historical data. Artificial intelligence-based CPS systems combine physiological signals, medical records, imaging data, genomics, and social and behavioral data from medical and social research. CPS systems use machine learning and mechanistic models to simulate disease progression, treatment response, and the impact of lifestyle on health outcomes. In a healthcare CPS system, the workflow begins with data acquisition from physical sensors, clinical workflows, and social context sources feeding the CPS system. The DT system analyzes and predicts the patient’s condition, while AI models generate personalized recommendations or alerts for clinicians, patients, or automated systems. Decisions or follow-up actions are then fed back to the physical system, for example, by adapting therapy, initiating interventions, or guiding patient behavior. This closed-loop interaction between the physical patient, the AI-powered digital twin, and healthcare systems enables safer, more personalized, and evidence-based care (Figure 9).
More broadly, CPS systems and patient DTs increase the efficiency of the entire healthcare system, reducing hospitalizations and supporting remote preventive care [31]. By detecting health risks early and improving patient monitoring, they enable interventions before hospitalization becomes necessary. These technologies also reduce the burden on medical staff by automating data analysis and providing more accurate diagnostic information [32]. Simultaneously, they improve access to care, allowing patients to benefit from consultations and health monitoring without the need for visits to medical facilities [33]. As a result, they support the development of a more sustainable, cost-effective, and proactive healthcare system.
However, their implementation poses significant ethical and privacy challenges, particularly in the areas of data ownership, algorithm transparency, and patient autonomy. It is crucial to determine who actually controls and manages health data, which is extremely sensitive and susceptible to abuse [34]. Transparency in the operation of algorithms is equally crucial, so that patients and healthcare professionals can understand the basis for diagnostic and therapeutic decisions [35]. Questions also arise about preserving patient autonomy when AI-based systems begin to influence treatment choices or suggest specific actions [35]. Consequently, it is essential to develop a robust regulatory and ethical framework that ensures the safe and responsible use of these technologies.
In this way, AI-based cyber–physical systems will revolutionize healthcare, transforming it into an intelligent, adaptive, and socially responsive ecosystem. This will be an environment where modern technologies seamlessly interact with biological processes, enabling a better understanding of human functioning [36]. At the same time, these systems will consider social and emotional factors, making care more empathetic and patient-centered [37]. Integrating such diverse elements will enable the creation of innovative models for diagnosis, therapy, and preventive care [38]. Ultimately, this will lead to healthcare that is not only technologically advanced but also deeply rooted in human needs and experiences [39,40].

3.3. UX in Healthcare CPS

Patient DTs, designed with UX principles in mind, provide more intuitive and patient-centric interactions than traditional healthcare IT systems. They simplify complex clinical information into understandable visualizations, helping patients feel more in control of their health. By integrating physiological, behavioral, and environmental data in real time, they offer personalized insights that reduce uncertainty and stress [41]. UX-optimized patient DTs also improve communication between patients and healthcare professionals, increasing transparency and trust [42]. Their ability to adapt to individual cognitive, emotional, and cultural needs increases engagement and adherence to care plans [43]. The interactive nature of these systems encourages active participation in self-care, which research shows improves quality of life. Improved accessibility features ensure usability for elderly patients and people with disabilities, reducing the digital divide [44]. Social research indicates that empathetic, patient-centered interfaces minimize frustration and cognitive burden during medical decision-making. Compared to traditional systems, patient DTs support continuous monitoring and early detection of issues, enabling proactive care that improves comfort and safety [45]. User-driven DT technologies for patients create a more helpful, transparent, and human digital environment in cyber–physical healthcare systems, also thanks to alternative ways of early diagnosis and communication [46,47].

3.4. Roadmap Toward Framework

The roadmap toward a standardized framework integrating social determinants of health (SDOH) with DTs architectures in cyber–physical healthcare systems begins with defining common data models that incorporate medical, behavioral, and socio-environmental factors. This requires establishing interoperable standards that enable clinical systems, sensors, and community-level datasets to seamlessly exchange SDOH-relevant information [47,48,49,50]. The next step is to embed validated medical and social research into AI models so that patient DT systems can simulate the impact of social conditions on disease progression and treatment outcomes. Ethical and governance guidelines should be established to ensure transparent, fair, and privacy-preserving use of SDOH data in real-time patient simulations. The roadmap also calls for the development of a robust validation framework that tests DT predictions across diverse populations to reduce bias. Integration layers connecting physical devices, electronic health records, and social datasets should be iteratively refined to support context-aware decision-making. Clinical workflows will require redesign to leverage SDOH-enhanced insights from DTs developmental research for diagnosis, care planning, and long-term monitoring. Interdisciplinary collaboration between engineers, clinicians, social scientists, and patient communities is essential to adapt the framework to real-world realities. Investments in education and digital literacy programs will support the equitable implementation of these advanced DT systems. This roadmap will establish a unified, standardized infrastructure in which patient DT systems incorporate social contexts, enabling more predictive, personalized, and socially relevant healthcare (Figure 10).
The roadmap is ambitious in that it outlines a vision for the future rather than reporting confirmed results or empirical findings. From the authors’ perspective, it proposes a unified, standardized infrastructure as a strategic goal, recognizing that current patient DT systems are fragmented and unevenly address the social context. By integrating social, clinical, and engineering dimensions, the roadmap identifies pathways toward more predictive and personalized healthcare, but does not claim that these benefits will be automatically achieved. Figure 10 illustrates conceptual unification and intended interactions, not proven system performance or clinical effectiveness. Therefore, this roadmap should be interpreted as a guiding framework that motivates coordinated research, standardization, and validation efforts, not as proof of a guaranteed outcome.
This way current healthcare CPSs integrate physiological, behavioral, and environmental data using connected sensors, medical devices, and digital platforms that stream real-time patient information into AI-based DTs models. Physiological data (heart rate, respiration, blood glucose, and imaging findings) are captured by wearable devices, implants, and clinical monitors to accurately model the patient’s internal state [51]. Behavioral data, including activity patterns, sleep patterns, medication adherence, and lifestyle habits, are collected from smartphones, wearables, and patient-reported tools, allowing for contextualization of daily health dynamics [52,53]. Environmental data, such as air quality, temperature, noise exposure, and socioeconomic indicators, are integrated from IoT systems, geospatial datasets, and public health records to capture external health influences [54]. Advanced data fusion algorithms combine these heterogeneous inputs into a unified representation optimized for personalized simulation. AI models continuously update the DT with new data, enabling it to predict disease progression, detect anomalies, and simulate response to interventions [55]. Medical research informs these computational models by combining biological mechanisms and clinical guidelines with measurable inputs, improving prediction accuracy. Social research provides insights into how behavior, social factors, and societal determinants influence patient outcomes, enabling more holistic simulations [56]. Cyber–physical infrastructure enables secure, low-latency communication between physical sensors and digital platforms, supporting real-time DT-patient synchronization. Together, these integrated systems create adaptive, context-aware patient DT that improves diagnosis, personalizes treatment, and improves the quality of care.
Medical and social research contributes to the development of patient-specific diagnostics by identifying clinically validated biomarkers and physiological pathways that improve the accuracy of predictive models. Clinical trials provide evidence-based parameters for modeling disease progression, enabling diagnostics to simulate treatment outcomes with greater confidence [57]. Research in genomics and pharmacology enhances personalization by enabling diagnostics to account for genetic variation and individual drug responses [58]. Behavioral health research illuminates how habits, medication adherence, and mental states influence clinical outcomes, helping diagnostics reflect real-world patient behavior [59]. Social determinants of health (SDOH) research reveals how factors such as housing, income, stress, and access to care significantly impact treatment outcomes, enabling diagnostics to more holistically model the patient context [60]. Epidemiological studies provide population-level models that help calibrate diagnostics for diverse demographic groups, reducing bias and increasing generalizability [61]. Human–computer interaction research is improving the usability and interpretability of DTs, increasing their clinical utility for both clinicians and patients [62]. Patient-reported outcomes research is enhancing personalization by capturing subjective experiences, such as pain, fatigue, and quality of life, that cannot be measured by sensors. Research on care pathways and clinical workflows is ensuring that cognitive therapy recommendations are aligned with real-world medical practice, making them feasible for implementation in CPS (Figure 11). Collectively, these medical and social studies are contributing to the development of cognitive therapy for patients that is more accurate, context-sensitive, and clinically relevant, including social robots as interfaces [63,64,65,66].
Several technical gaps limit the implementation of AI-based DTs in real-world healthcare settings, including insufficient interoperability between sensors, electronic health records, and environmental data sources. Many DT models still struggle with real-time data fusion, which hinders the generation of continuously updated and clinically reliable simulations. From a methodological perspective, biased or incomplete datasets hinder DTs’ generalization to diverse populations and accurate representation of the social determinants of health. Ethical concerns arise from the use of sensitive physiological, behavioral, and socioeconomic data, especially when frameworks for managing privacy, consent, and algorithm transparency remain immature. Clinical validation processes are often inconsistent, resulting in DTs’ predictions not yet meeting the rigorous medical standards required for diagnostic or therapeutic purposes. From a well-being perspective, poorly designed interfaces can overwhelm patients with complex data, reducing their trust and hindering their interaction with the system. UX gaps persist because many DT platforms are not tailored to the cognitive, emotional, and accessibility needs of diverse patient populations. Operational challenges (integration into clinical processes and limited training of healthcare professionals in AI-based tools) limit their implementation in practice [67]. Cybersecurity vulnerabilities create additional risks in cyber–physical environments, where compromised data streams can disrupt DTs [68]. These vulnerabilities limit accuracy, usability, and ethical acceptability, slowing the effective implementation of DT-based healthcare systems.
Interdisciplinary evidence can improve healthcare CPS by combining clinical research with engineering knowledge to create accurate, physiologically based patient-specific DTs. Social science evidence helps designers incorporate behavioral patterns and social determinants of health, ensuring DTs better reflect real-world patient contexts. Human–computer interaction research informs the design of user-centered interfaces, providing patients and healthcare professionals with intuitive interpretations of DT results. Data science and artificial intelligence expertise enable more robust validation methods that test DT performance across diverse datasets and real-world scenarios. Public health research provides population-level benchmarks that help scale DT models to diverse demographic and social environments. Ethical and legal research provides a framework for trust, transparency, and privacy, enabling more secure scaling of cyber–physical systems using sensitive patient data. Evidence from implementation studies guides the integration of DTs into clinical processes, reducing disruptions and streamlining implementation. Behavioral medicine research supports proactive system design by identifying early warning indicators and adherence patterns that DTs should monitor. Biomedical engineering evidence improves sensor reliability, enabling more accurate and continuous collection of physiological data. Collectively, these interdisciplinary advancements create scalable, patient-centric cyber–physical systems capable of predictive and proactive healthcare delivery [69].

4. Discussion

The limited number of publications makes it difficult to compare the approach described above with the approaches developed by other researchers. The main limitations, implications, and directions for further research are presented below.

4.1. Limitations

Current research and clinical practice using AI-based patient DTs in cyber healthcare systems face significant limitations in data integration, as physiological, behavioral, and environmental data often remain fragmented or inconsistent. Many DT models rely on biased or unrepresentative datasets, limiting their accuracy and generalizability across diverse populations and social contexts. Clinical validation standards are still underdeveloped, resulting in DT models lacking the rigorous scientific evidence necessary for routine medical decision-making. Ethical and legal frameworks have not kept pace with rapid technological advances, creating unresolved issues regarding privacy, fairness, and the transparent use of sensitive patient data. In clinical practice, physicians and patients often struggle with complex interfaces and insufficient training, limiting the usability and acceptance of DT-based systems. Furthermore, scalability remains limited due to technical interoperability issues, uneven infrastructure, and the lack of standardized architectures enabling the integration of medical and social research with DTs design [70].
Statements such as “improving the quality of healthcare” or “enabling more effective care” should be clearly stated as conclusions drawn from the reviewed literature, not as results proven by direct clinical validation in this study. In peer-reviewed publications, these claims typically stem from reports of improved surrogate indicators (e.g., increased decision support accuracy, reduced response time, improved patient monitoring detail, or optimized resource allocation) rather than from large-scale randomized clinical trials or longitudinal outcome studies. Therefore, it is important to clarify that bibliometric analysis identifies research trends, thematic emphases, and intended clinical benefits as described by the authors of the primary studies, not empirically verified healthcare outcomes. Adding this clarification helps distinguish predicted or potential impact from proven clinical effectiveness, which remains an open research challenge for AI-based DTs and CPS in healthcare. Such clarity reflects the current level of maturity in this field, where technological feasibility and conceptual frameworks often precede robust clinical validation. Explicitly stating this limitation reinforces the scientific nature of the manuscript by preventing overgeneralization and aligning conclusions with the methodological scope of bibliometric evidence rather than clinical evidence.

4.2. Technological Implications

Further development of CPS in healthcare will require more advanced sensor technologies capable of delivering high-quality, continuous data to AI-based DTs. Advances in edge computing will reduce latency, enabling patients and physicians to receive real-time feedback. Improved interoperability standards will enable CPS to seamlessly integrate various medical devices, wearable sensors, and electronic health records. AI models in DTs will become more transparent and interpretable, increasing user trust and clinical acceptance [71]. UX design will prioritize intuitive interfaces that simplify complex simulations and predictions for non-technical users. Patients will benefit from personalized dashboards that clearly visualize their health status and provide actionable guidance based on both medical and social knowledge [72]. Medical professionals will gain decision-support tools that dynamically adapt to specific patient behaviors, risks, and treatment responses. Improved data privacy technologies will be essential to maintaining user trust while enabling the secure integration of sensitive social and behavioral information. CPS will also support remote and distributed care models, improving accessibility and reducing the burden on healthcare systems [73]. In this way, CPS technological advancements will lead to a more responsive, transparent, and user-centric healthcare ecosystem, supported by AI-driven DTs.

4.3. Economic Implications

Despite significant implementation costs, further development of CPS with AI-based DTs will lower long-term healthcare costs by shifting care from reactive treatment to early detection and prevention. Real-time monitoring and predictive modeling will reduce hospital readmissions and emergency interventions, generating significant savings for healthcare providers. Integrating medical and social research will enable more efficient resource allocation by identifying high-risk patients and tailoring interventions accordingly [74]. Investments in advanced sensors, data infrastructure, and AI platforms will be significant initially, but economies of scale will lower costs as the technology matures [75]. Automation of routine tasks based on CPS will reduce administrative burdens and allow clinical staff to provide higher-value care. Personalized treatment plans based on DTs can shorten recovery times and reduce reliance on expensive, generic therapies. Remote care, supported by CPS, will expand access to care while lowering per-patient costs, particularly in rural or underserved areas. More accurate population-level analyses will support better healthcare budgeting and planning. The development of CPS-related technologies will stimulate new markets for digital health services, analytics, and medical device innovation [76]. Ultimately, these systems will promote a more cost-effective, outcomes-oriented healthcare economy based on continuous data-driven decision-making.

4.4. Social Implications

Further development of CPS in healthcare will empower patients by providing them with better insights into their health status through continuously updated DTs. These systems will support more equitable healthcare by addressing the social determinants of health, enabling tailored interventions for underserved and vulnerable populations. Widespread adoption of CPS could shift societal expectations toward proactive health management rather than episodic treatment. As remote monitoring becomes more widespread, communities will enjoy improved access to care, regardless of geographic and socioeconomic barriers [77]. However, increasing reliance on digital systems will raise concerns about privacy, surveillance, and the ethical use of sensitive behavioral and social data, especially in aging societies in developed countries. Digital health literacy will become essential, and disparities in access to technology could exacerbate existing gaps if not addressed. Early detection and personalized intervention facilitated by CPS will help reduce the societal burden of chronic diseases, improving the overall well-being of the population. Families and caregivers will benefit from more transparent and continuous information about patients’ health, enabling better support. At the same time, trust in healthcare institutions may increasingly depend on transparent AI governance and responsible data management [78]. The impact of CPS on society will mean a transformation toward more connected, informed, and participatory models of healthcare.

4.5. Ethical and Legal Implications

The continued development of CPS in healthcare will heighten ethical concerns regarding the collection, storage, and use of sensitive physiological, behavioral, and social data. AI-based DTs raise questions about informed consent, as patients may not fully understand how their continuously updated virtual models are used in clinical and non-clinical decisions. Ensuring data privacy and preventing unauthorized access are becoming key legal obligations, especially when integrating social determinants of health [79]. Algorithmic biases pose significant ethical challenges, as DTs trained on incomplete or unrepresentative datasets can generate unfair or harmful predictions. Legal frameworks will need to clearly define liability in cases where CPS-based recommendations contribute to medical errors or adverse events. The cross-border flow of data in global healthcare environments complicates compliance with diverse privacy regulations and cybersecurity standards. Demands for transparency and explainability of AI models will increase as medical professionals and patients demand understandable and auditable decision-making processes [80]. Ethical guidelines must consider the balance between patient autonomy and automated interventions triggered by real-time CPS monitoring. The use of DTs in insurance, employment, and social profiling raises concerns about discrimination and abuse beyond the clinical context. Robust regulatory oversight and ethical governance will be essential to ensure that CPS and DTs improve healthcare without compromising human rights, dignity, or justice.

4.6. Directions for Further Research

Key directions for future research on CPS in healthcare include developing more accurate and adaptive AI-based decision support devices that reflect real-time physiological and behavioral changes. Researchers must develop multimodal sensor technologies that seamlessly integrate medical, social, and environmental data streams [81]. The growing need for standardized frameworks and ontologies will ensure interoperability between devices, clinical systems, and DTs platforms. Future research should explore methods for modeling complex conditions, such as multimorbidity and mental health, within DTs. UX research is crucial for designing interfaces that communicate complex prognoses in a transparent and patient-friendly manner. Further development of ethical and privacy-preserving data architectures is necessary to support reliable and secure CPS implementation. Validation studies are necessary to assess the safety, reliability, and effectiveness of CPS-based decision support tools, also within larger environments such as smart cities and smart territories [82]. Social research should focus on understanding how diverse populations interact with CPS technologies and how digital equity can be achieved. Researchers also need to explore how CPS can be integrated into routine clinical processes without increasing cognitive and administrative burdens [83]. Simulation-based research using DTs should be expanded to assess treatment strategies, optimize health system performance, and support personalized, proactive care [84].

5. Conclusions

The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context.
CPSs augmented with AI-based patient DTs are pioneering a breakthrough for personalized, predictive, and context-aware healthcare. By integrating medical evidence with behavioral data and social determinants of health, these systems offer a more holistic understanding of patient needs and trajectories. Despite promising results, significant gaps remain in data interoperability, validation standards, and the ethical management of sensitive data from multiple sources. Addressing these limitations requires deep, interdisciplinary collaboration between clinicians, engineers, sociologists, ethicists, and patient communities. Through coordinated research, design, and policy efforts, cyber–physical systems based on DTs can evolve toward safe, equitable, and clinically practical solutions. These innovations have the potential to transform healthcare delivery, enabling proactive, patient-centered care grounded in both biological and social realities.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app16010318/s1, File S1: PRISMA 2020 Checklist (partial only) [85].

Author Contributions

Conceptualization, E.M. and D.M.; methodology, E.M., U.R.-Ł., J.M., E.P. and D.M.; software, D.M.; validation, E.M., U.R.-Ł., J.M., E.P. and D.M.; formal analysis, E.M., U.R.-Ł., J.M., E.P. and D.M.; investigation, E.M., U.R.-Ł., J.M., E.P. and D.M.; resources, E.M., U.R.-Ł., J.M., E.P. and D.M.; data curation, E.M., U.R.-Ł., J.M., E.P. and D.M.; writing—original draft preparation, E.M., U.R.-Ł., J.M., E.P. and D.M.; writing—review and editing, E.M., U.R.-Ł., J.M., E.P. and D.M.; visualization, D.M.; supervision, E.M.; project administration, E.M.; funding acquisition, E.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a grant for the research potential of Kazimierz Wielki University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

None datasets were generated during the study.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial intelligence
CPSCyber–physical system
DTDigital twin
MLMachine learning
SDOHSocial determinants of health
UXUser eXperience

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Figure 1. PRISMA flow diagram of the review process (based on ten selected PRISMA 2020 items). n—number of suitable publications according to the criteria, eligibility phase was included in the last step of the selection phase.
Figure 1. PRISMA flow diagram of the review process (based on ten selected PRISMA 2020 items). n—number of suitable publications according to the criteria, eligibility phase was included in the last step of the selection phase.
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Figure 2. Documents by year.
Figure 2. Documents by year.
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Figure 3. Documents by type.
Figure 3. Documents by type.
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Figure 4. Documents by subject area.
Figure 4. Documents by subject area.
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Figure 5. Documents by country/territory.
Figure 5. Documents by country/territory.
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Figure 6. CPS evolution (own figure based on [24]).
Figure 6. CPS evolution (own figure based on [24]).
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Figure 7. Building patient-specific DTs in healthcare CPS.
Figure 7. Building patient-specific DTs in healthcare CPS.
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Figure 8. Data flow within the CPS.
Figure 8. Data flow within the CPS.
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Figure 9. CPS with DT workflow.
Figure 9. CPS with DT workflow.
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Figure 10. Roadmap toward standardized frameworks integrating social determinants of health (SDOH) with Digital Twin architectures in cyber–physical healthcare systems.
Figure 10. Roadmap toward standardized frameworks integrating social determinants of health (SDOH) with Digital Twin architectures in cyber–physical healthcare systems.
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Figure 11. CPS layers and selected technologies, where: 5G—5. generation networks, RFID—Radio-Frequency IDentification, GPS—Global Positioning System.
Figure 11. CPS layers and selected technologies, where: 5G—5. generation networks, RFID—Radio-Frequency IDentification, GPS—Global Positioning System.
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Table 1. Bibliometric analysis procedure (own approach).
Table 1. Bibliometric analysis procedure (own approach).
Stage NameTasks
Defining
research goal(s)		Defining goals of the bibliometric analysis
Selecting databases and data collectionsSelecting appropriate dataset(s)
and developing research queries according to the study goals
Data preprocessingCleaning the collected dataset(s)
to remove duplicates and irrelevant records
Bibliometric software (WoS, Scopus, dblp, PubMed, and Biblioshiny) selectionChoosing suitable bibliometric software/tools for analysis
Data analysisDescription, author, journal, area, topics, institution, country, etc.
Visualization
(if possible)		Visualizing the analysis results to present insights
Interpretation
and discussion		Interpreting findings in the context of the research goals and RQs
Table 2. Databases search query (* for word variations and ? for alternative spellings).
Table 2. Databases search query (* for word variations and ? for alternative spellings).
Parameter/FeatureDetailed Description
Inclusion criteriaBooks and chapters in books, articles (original, reviews, communication, editorials), and conference proceedings, in English
Exclusion criteriaOlder than 10 years, letters, conference abstracts without full text, other languages than English
Exact keywords usedHealthcare AND (“cyber-physical” OR “cyber physical”) AND “digital twin” AND (“artificial intelligence” OR “machine learning”)
Used field codes (WoS)“Subject” field (consisting of title, abstract, keyword plus and other keywords): healthcare AND (“cyber-physical” OR “cyber physical”) AND “digital twin” AND (“artificial intelligence” OR “machine learning”)
Used field codes (Sopus)Article title, abstract and keywords: healthcare AND (“cyber-physical” OR “cyber physical”) AND “digital twin” AND (“artificial intelligence” OR “machine learning”)
Used field codes (PubMed)Manually: healthcare AND (“cyber-physical” OR “cyber physical”) AND “digital twin” AND (“artificial intelligence” OR “machine learning”)
Used field codes (dblp)Manually: healthcare AND (“cyber-physical” OR “cyber physical”) AND “digital twin” AND (“artificial intelligence” OR “machine learning”)
Boolean operators usedYes, e.g., (“cyber-physical” OR “cyber physical”)
Applied filtersResults refined by publication year, document type (e.g., articles, reviews), and subject area (e.g., industry, engineering).
Iteration and validation
options		Queries are run iteratively, refined based on results, and validated by ensuring that relevant publications appear among the top results
Leverage truncation
and wildcards used		Used symbols like * for word variations and ? for alternative spellings
Table 3. Summary of bibliographic analysis results (WoS, Scopus, PubMed, dblp, 47 publications [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69]).
Table 3. Summary of bibliographic analysis results (WoS, Scopus, PubMed, dblp, 47 publications [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69]).
Parameter/FeatureValue
Leading types of publicationArticle (28.60%), Book chapter (25.70%), Conference review (17.1%)
Leading areas of scienceComputer science (37.20%), Engineering (22.10%), Mathematics (11.60%)
Leading countriesIndia (22.86%), USA (17.14%), China (8.57%)
Leading scientistsNone observed
Leading affiliationsNone observed
Leading funders (where information available)None observed
Sustainable development goalsIndustry Innovation and Infrastructure (5.71%), Responsible Production and Consumption (5.71%)
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MDPI and ACS Style

Mikołajewska, E.; Rogalla-Ładniak, U.; Masiak, J.; Panas, E.; Mikołajewski, D. Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients. Appl. Sci. 2026, 16, 318. https://doi.org/10.3390/app16010318

AMA Style

Mikołajewska E, Rogalla-Ładniak U, Masiak J, Panas E, Mikołajewski D. Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients. Applied Sciences. 2026; 16(1):318. https://doi.org/10.3390/app16010318

Chicago/Turabian Style

Mikołajewska, Emilia, Urszula Rogalla-Ładniak, Jolanta Masiak, Ewelina Panas, and Dariusz Mikołajewski. 2026. "Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients" Applied Sciences 16, no. 1: 318. https://doi.org/10.3390/app16010318

APA Style

Mikołajewska, E., Rogalla-Ładniak, U., Masiak, J., Panas, E., & Mikołajewski, D. (2026). Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients. Applied Sciences, 16(1), 318. https://doi.org/10.3390/app16010318

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