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The Role of AI in Predictive and Prescriptive Healthcare
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The Role of AI in Predictive and Prescriptive Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 4507

Special Issue Editor

Dr. Matthew Chin Heng Chua

E-Mail Website
Guest Editor
Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Interests: artificial intelligence; data science; medical systems; medical informatics; robotics systems

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your research to a Special Issue of MDPI Healthcare entitled “The Role of AI in Predictive and Prescriptive Healthcare”. Artificial intelligence (AI) is rapidly transforming the healthcare landscape, with applications impacting disease diagnosis, treatment planning, and personalized medicine. This burgeoning field holds immense promise for revolutionizing healthcare delivery and improving patient outcomes.

This Special Issue aims to showcase the latest advancements and explore the diverse applications of AI and data science in healthcare, medicine, and population health. We welcome submissions that align with the journal's focus on high-quality, impactful research relevant to healthcare professionals and researchers.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Machine learning in medical diagnosis and prediction;
  • AI-powered medical image analysis (e.g., radiology, pathology);
  • Natural language processing for clinical decision support;
  • AI for personalized medicine and patient stratification;
  • AI-driven wearables and remote patient monitoring;
  • Challenges of AI in healthcare.

We encourage submissions of original research articles, comprehensive reviews, and short communications addressing these themes.

I look forward to receiving your contributions.

Dr. Matthew Chin Heng Chua
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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
  • machine learning
  • deep learning
  • healthcare
  • medicine
  • diagnosis
  • treatment
  • personalized medicine
  • medical imaging
  • clinical decision support

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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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19 pages, 254 KB  
Article
The Application of Artificial Intelligence in Acute Prescribing in Homeopathy: A Comparative Retrospective Study
by Rachael Doherty, Parker Pracjek, Christine D. Luketic, Denise Straiges and Alastair C. Gray
Healthcare 2025, 13(15), 1923; https://doi.org/10.3390/healthcare13151923 - 6 Aug 2025
Viewed by 1230
Abstract
Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute [...] Read more.
Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints. Full article
(This article belongs to the Special Issue The Role of AI in Predictive and Prescriptive Healthcare)
17 pages, 3873 KB  
Article
Prediction of Post-Bath Body Temperature Using Fuzzy Inference Systems with Hydrotherapy Data
by Feng Han, Minghui Tang, Ziheng Zhang, Kenji Hirata, Yoji Okugawa, Yunosuke Matsuda, Jun Nakaya, Katsuhiko Ogasawara and Kohsuke Kudo
Healthcare 2025, 13(9), 972; https://doi.org/10.3390/healthcare13090972 - 23 Apr 2025
Viewed by 630
Abstract
Background/Objectives: Widely known for its therapeutic benefits, hydrotherapy utilizes water’s physical properties, such as temperature, hydrostatic pressure, and viscosity, to influence physiological responses. Among these, body temperature modulation plays a crucial role in enhancing circulatory function, muscle relaxation, and metabolic processes. While hydrotherapy [...] Read more.
Background/Objectives: Widely known for its therapeutic benefits, hydrotherapy utilizes water’s physical properties, such as temperature, hydrostatic pressure, and viscosity, to influence physiological responses. Among these, body temperature modulation plays a crucial role in enhancing circulatory function, muscle relaxation, and metabolic processes. While hydrotherapy can improve systemic health, particularly cardiac function, improper temperature control poses risks, especially for vulnerable populations such as the elderly or individuals with thermoregulatory impairments. Therefore, accurately predicting post-bath body temperature is essential for ensuring safety and optimizing therapeutic outcomes. Methods: This study explored the use of fuzzy inference systems to predict post-bath body temperature, leveraging an adaptive neuro-fuzzy inference system, evolutionary fuzzy inference system (EVOFIS), and enhanced Takagi-Sugeno fuzzy system. These models were compared with random forest and support vector machine models using hydrotherapy-related data. Results: The results show that EVOFIS outperformed other models, particularly in predicting deep body temperature, which is clinically significant as it is closely linked to core physiological regulation. Conclusions: The ability to accurately forecast deep-temperature dynamics enables proactive management of hyperthermia risk, supporting safer hydrotherapy practices for at-risk groups. These findings highlight the potential of FIS-based models for non-invasive temperature prediction, contributing to enhanced safety and personalization in hydrotherapy applications. Full article
(This article belongs to the Special Issue The Role of AI in Predictive and Prescriptive Healthcare)
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Review

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32 pages, 1126 KB  
Review
Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review
by Syed Raza Abbas, Huiseung Seol, Zeeshan Abbas and Seung Won Lee
Healthcare 2025, 13(14), 1642; https://doi.org/10.3390/healthcare13141642 - 8 Jul 2025
Viewed by 2051
Abstract
Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures [...] Read more.
Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article
(This article belongs to the Special Issue The Role of AI in Predictive and Prescriptive Healthcare)
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Other

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28 pages, 765 KB  
Systematic Review
Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges
by Qaiser Abbas, Woonyoung Jeong and Seung Won Lee
Healthcare 2025, 13(17), 2154; https://doi.org/10.3390/healthcare13172154 - 29 Aug 2025
Abstract
Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes [...] Read more.
Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical domains, including radiology, oncology, neurology, and critical care. Model-agnostic techniques such as visualization models like Gradient-weighted Class Activation Mapping (Grad-CAM) and attention mechanisms dominated in imaging and sequential data tasks. Results: However, there are still gaps in user-friendly evaluation, methodological transparency, and ethical issues, as seen by the absence of research that evaluated explanation fidelity, clinician trust, or usability in real-world settings. In order to enable responsible AI implementation in healthcare, our analysis emphasizes the necessity of longitudinal clinical validation, participatory system design, and uniform interpretability measures. Conclusions: This review offers a thorough analysis of the state of XAI practices in CDSSs today, identifies methodological and practical issues, and suggests a path forward for AI solutions that are open, moral, and clinically relevant. Full article
(This article belongs to the Special Issue The Role of AI in Predictive and Prescriptive Healthcare)
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