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Artificial Intelligence-Based Digital Health Emerging Technologies
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Artificial Intelligence-Based Digital Health Emerging Technologies

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3389

Special Issue Editor

Dr. Phillip Olla

E-Mail Website
Guest Editor
College of Health Professionals, University of Detroit Mercy, Detroit, MI 48221, USA
Interests: information systems; artificial intelligence; digital health; healthcare innovation; mobile health

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) in digital health is rapidly transforming healthcare by improving diagnostics, enhancing patient care, and optimizing operational efficiency. AI-driven technologies, such as predictive analytics, remote monitoring, and AI-powered diagnostic tools, are not only reshaping how care is delivered, but also expanding healthcare access through telehealth platforms and mobile health solutions. Despite these advancements, many challenges remain, including ensuring data privacy and integrating AI into existing healthcare systems.

This Special Issue aims to bring together innovative research that explores the latest developments in AI-based digital health technologies. Contributions that examine the intersection of AI with digital health systems, highlight real-world applications, and discuss the challenges in scaling these technologies are particularly welcome. This Special Issue will serve as a platform for researchers, clinicians, and technologists to present cutting-edge solutions that address the complex demands of modern healthcare.

Topics of interest include, but are not limited to, the following:

  • AI in remote patient monitoring and telehealth solutions;
  • AI-driven diagnostics and personalized medicine;
  • AI applications in health data analytics and predictive modeling;
  • AI-enhanced mobile health (mHealth) technologies;
  • Case studies and best practices in implementing AI in digital health;
  • Machine learning and deep learning methods for clinical decision support;
  • Data integration and privacy in AI healthcare systems;
  • AI for workforce management and scheduling optimization.

Dr. Phillip Olla
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • digital health
  • personalized medicine
  • mobile health
  • clinical decision support
  • healthcare systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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24 pages, 4298 KB  
Article
Machine Learning-Enhanced Architecture Model for Integrated and FHIR-Based Health Data
by Nadia Brancati, Teresa Conte, Simona De Pietro, Martina Russo and Mario Sicuranza
Information 2025, 16(12), 1054; https://doi.org/10.3390/info16121054 - 2 Dec 2025
Viewed by 181
Abstract
The widespread fragmentation of patient information across heterogeneous systems and the lack of standardized integration mechanisms hinder efficient and comprehensive medical diagnostics. To address these limitations, this work presents an architecture framework designed to support physicians in the diagnostic process by integrating clinical [...] Read more.
The widespread fragmentation of patient information across heterogeneous systems and the lack of standardized integration mechanisms hinder efficient and comprehensive medical diagnostics. To address these limitations, this work presents an architecture framework designed to support physicians in the diagnostic process by integrating clinical and socio-health information (patient medical histories), structured documents extracted from Health Information System (HIS), and data automatically extracted from diagnostic images using Artificial Intelligence (AI) techniques. The proposed architecture is made by several modules, in particular a Decision Support System (DSS) that enables risk assessment related to specific patient’s clinical conditions. In addition, the clinical information retrieved is aggregated, standardized, and transmitted to external systems for follow up. Standardization and data interoperability are ensured through the adoption of the international HL7 Fast Healthcare Interoperability Resources (FHIR) standard, which facilitates seamless connection with HIS. An Android application has been developed to communicate with different HISs in order to: (i) retrieve information, (ii) aggregate clinical data, (iii) calculate patient risk scores using AI algorithms, (iv) display results to healthcare professionals, and (v) generate and share relevant clinical information with external systems in a standardized format. To demonstrate architecture’s applicability, a case study on breast cancer diagnosis is presented. In this context, an AI-based Risk Assessment module was developed using the Breast Ultrasound Images Dataset (BUSI), which includes benign, malignant, and normal cases. Machine Learning algorithms were applied to perform the classification task. Model performance was evaluated using a 4-fold cross-validation strategy to ensure robustness and generalizability. The best results were achieved using the Multilayer Perceptron method, with a competitive F1-score of 0.97. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Digital Health Emerging Technologies)
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23 pages, 1559 KB  
Article
A Layered Entropy Model for Transparent Uncertainty Quantification in Medical AI: Advancing Trustworthy Decision Support in Small-Data Clinical Settings
by Sandeep Bhattacharjee and Sanjib Biswas
Information 2025, 16(10), 875; https://doi.org/10.3390/info16100875 - 9 Oct 2025
Viewed by 627
Abstract
Smaller data environments with expert systems are generally driven by the need for interpretable reasoning frameworks, such as fuzzy rule-based systems (FRBS), which cannot often quantify epistemic uncertainty during decision-making. This study proposes a novel Layered Entropy Model (LEM) comprising three semantic layers: [...] Read more.
Smaller data environments with expert systems are generally driven by the need for interpretable reasoning frameworks, such as fuzzy rule-based systems (FRBS), which cannot often quantify epistemic uncertainty during decision-making. This study proposes a novel Layered Entropy Model (LEM) comprising three semantic layers: Membership Function Entropy (MFE), Rule Activation Entropy (RAE), and System Output Entropy (SOE). Shannon entropy is applied at each layer to enable granular diagnostic transparency throughout the inference process. The approach was evaluated using both synthetic simulations and a real-world case study on the PIMA Indian Diabetes dataset. In the real data experiment, the system produced sharp, fully confident decisions with zero entropy at all layers, yielding an Epistemic Confidence Index (ECI) of 1.0. The proposed framework maintains full compatibility with conventional Type-1 FRBS design while introducing a computationally efficient and fully interpretable uncertainty quantification capability. The results demonstrate that LEM can serve as a powerful tool for validating expert knowledge, auditing system transparency, and deployment in high-stakes, small-data decision domains, such as healthcare, safety, and finance. The model contributes directly to the goals of explainable artificial intelligence (XAI) by embedding uncertainty traceability within the reasoning process itself. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Digital Health Emerging Technologies)
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Review

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42 pages, 1752 KB  
Review
Artificial Intelligence and Machine Learning in the Diagnosis and Prognosis of Diseases Through Breath Analysis: A Scoping Review
by Christos Kokkotis, Serafeim Moustakidis, Stefan James Swift, Flora Kontopidou, Ioannis Kavouras, Anastasios Doulamis and Stamatios Giannoukos
Information 2025, 16(11), 968; https://doi.org/10.3390/info16110968 - 10 Nov 2025
Viewed by 914
Abstract
Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent [...] Read more.
Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent advancements in chemical sensing, mass spectrometry, and spectroscopy have improved the ability to identify these biomarkers; however, traditional statistical approaches often struggle to handle the complexities of breath data. Artificial intelligence (AI) and machine learning (ML) have revolutionized breath analysis by uncovering intricate patterns among volatile breath markers, enhancing diagnostic precision, and facilitating real-time disease identification. Despite significant progress, challenges remain, including issues with data standardization, model interpretability, and the necessity for extensive and varied datasets. This study reviews the applications of ML in analyzing breath volatile organic compounds, highlighting methodological shortcomings and obstacles to clinical validation. A thorough literature review was performed using the PubMed and Scopus databases, which included studies that focused specifically on the role of machine learning in disease diagnosis and incidence prediction via breath analysis. Among the 524 articles reviewed, 97 satisfied the specified inclusion criteria. The selected studies applied ML techniques, fell within the scope of this review, and emphasize the potential of ML models for non-invasive diagnostics. The findings indicate that traditional ML methods dominate, while ensemble methods are on the rise, and deep learning (DL) techniques (especially CNNs and LSTMs) are increasingly used for classifying respiratory diseases. Techniques for feature selection (such as PCA and ML-based methods) were frequently implemented, though challenges related to explainability and data standardization persist. Future studies should focus on enhancing model transparency and developing methods to further integrate AI into the clinical setting to facilitate early disease detection and advance precision medicine. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Digital Health Emerging Technologies)
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Other

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19 pages, 690 KB  
Systematic Review
Multimodal Models in Healthcare: Methods, Challenges, and Future Directions for Enhanced Clinical Decision Support
by Md Kamrul Siam, Md Jobair Hossain Faruk, Bofan He, Jerry Q. Cheng and Huanying Gu
Information 2025, 16(11), 971; https://doi.org/10.3390/info16110971 - 10 Nov 2025
Viewed by 1352
Abstract
Decision-making in modern healthcare increasingly relies on integrating a variety of data sources, including patient demographics, medical imaging, laboratory results, clinical narratives, and temporal data, all of which are difficult for traditional computational methodologies to accurately predict. This paper evaluates the latest methodologies [...] Read more.
Decision-making in modern healthcare increasingly relies on integrating a variety of data sources, including patient demographics, medical imaging, laboratory results, clinical narratives, and temporal data, all of which are difficult for traditional computational methodologies to accurately predict. This paper evaluates the latest methodologies that integrate diverse data types, including photographs, clinical notes, temporal measurements, and structured tables, through techniques such as feature amalgamation, prioritization of essential information, and utilization of graphs. We also assess pre-training, fine-tuning, and comprehensive evaluation of model generation procedures. By synthesizing findings from 50 of 91 peer-reviewed papers published between 2020 and 2024, we demonstrate that the integration of structured and unstructured data significantly improves performance in tasks like diagnosis, prognosis prediction, and personalized treatment. This review combines substantial multimodal datasets and applications across several therapeutic domains while addressing critical issues such as data heterogeneity, scalability, interpretability, and ethical considerations. This paper highlights the transformative potential of multimodal models in improving clinical decision support, providing a framework for future research to advance precision medicine and enhance healthcare outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Digital Health Emerging Technologies)
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