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Applied Sciences
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Artificial Intelligence in Healthcare: Status, Prospects and Future
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Artificial Intelligence in Healthcare: Status, Prospects and Future

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 2890

Special Issue Editors

Dr. Massimo Esposito

E-Mail Website
Guest Editor
Institute of Computing and High-Performance Networks, Department of Engineering, ICT and Technology for Energy and Transport, National Research Council (CNR), Naples, Italy
Interests: artificial intelligence; decision support systems; natural language processing and knowledge engineering
Dr. Aniello Minutolo

E-Mail Website
Guest Editor
Institute of Computing and High-Performance Networks, Department of Engineering, ICT and Technology for Energy and Transport, National Research Council (CNR), Naples, Italy
Interests: artificial intelligence; decision support systems; natural language processing and knowledge engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Umberto Maniscalco

E-Mail Website
Guest Editor
Institute of Computing and High-Performance Networks, Department of Engineering, ICT and Technology for Energy and Transport, National Research Council (CNR), Naples, Italy
Interests: artificial intelligence; robotics; social robotics; healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Ciro Mennella

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking (ICAR), Research National Council of Italy (CNR), Naples, Italy
Interests: precision and predictive medicine; artificial intelligence; cyber-physical systems for healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is a powerful and disruptive area of computer science that has recently attracted considerable interest from healthcare stakeholders. AI applications in healthcare can potentially address key challenges in clinical practice and significantly enhance healthcare outcomes.

More specifically, AI is considered to improve personalized and predictive medicine, improve the decision-making process, assist more clinicians, and optimize resource allocation, providing cost-effective services.

Considering the rapidly evolving research landscape on AI systems for healthcare applications, this Special Issue aims to gather the latest efforts and insights from the research community actively engaged in this field.

We invite you to contribute to this special issue aimed at advancing research on AI systems for healthcare applications.

This Special Issue seeks to explore the current state of AI applications in healthcare while also examining future implications and challenges. We encourage contributions that encompass technical, experimental, methodological, and conceptual insights into AI for diagnosis, treatment, and rehabilitation.

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

  • AI-driven decision support systems;
  • AI-driven techniques to optimize clinical workflow;
  • Machine learning and deep learning applications;
  • Predictive and precision medicine based on AI analytical methods;
  • AI and personalized medicine;
  • AI-driven therapy and drug discovery;
  • AI and assistive systems;
  • AI-based monitoring and surveillance systems;
  • Computer vision for clinical applications;
  • AI for telemedicine and telerehabilitation;
  • Natural language processing and AI agents;
  • Data collection;
  • Data security and privacy issues;
  • Ethical challenges in AI (e.g., fairness, trustworthiness, transparency);
  • Legislation and regulatory frameworks for AI in healthcare.

Dr. Massimo Esposito
Dr. Aniello Minutolo
Dr. Umberto Maniscalco
Dr. Ciro Mennella
Guest Editors

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. Applied Sciences 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 2400 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
  • healthcare
  • decision support system

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Published Papers (2 papers)

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23 pages, 781 KB  
Article
Deep Reinforcement Learning-Driven Adaptive Prompting for Robust Medical LLM Evaluation
by Dong Ding, Wang Xi, Zenghui Ding and Jianqing Gao
Appl. Sci. 2026, 16(3), 1514; https://doi.org/10.3390/app16031514 - 2 Feb 2026
Viewed by 477
Abstract
The accurate and reliable evaluation of large language models (LLMs) in medical domains is critical for real-world clinical deployment, automated medical reasoning, and patient safety. However, the evaluation process is highly sensitive to prompt design, and prevalent reliance on fixed or randomly sampled [...] Read more.
The accurate and reliable evaluation of large language models (LLMs) in medical domains is critical for real-world clinical deployment, automated medical reasoning, and patient safety. However, the evaluation process is highly sensitive to prompt design, and prevalent reliance on fixed or randomly sampled prompt policies often fails to dynamically adapt to clinical context, question complexity, or evolving safety requirements. This article presents a novel reinforcement learning-based framework for multi-prompt selection, which dynamically optimizes prompt policy per input for medical LLM evaluation across the Medical Knowledge Question-Answering dataset (MKQA), the Medical Multiple-Choice Question dataset (MCQ), and the Doctor-Patient Dialogue dataset. We formulate prompt selection as a Markov Decision Process (MDP) and employ a deep Q-Network (DQN) agent to maximize a reward signal incorporating textual accuracy, domain terminology coverage, safety, and dialogue relevance. Experiments on three medical LLM benchmarks demonstrate consistent improvements in composite reward (e.g., a 6.66% increase in MKQA vs. Random Baseline, and a 2.41% increase in Dialogue vs. Fixed Baseline) when compared to baselines. This was accompanied by robust enhancements in Safety (e.g., achieving 1.0000 in MKQA, a 5.26% increase vs. Fixed Baseline; and a 5.03% increase in Dialogue vs. Fixed Baseline) and substantial gains in Medical Terminology Coverage (e.g., a 74.61% increase in MKQA vs. Fixed Baseline, and a 9.13% increase in MCQ vs. Fixed Baseline) when compared to baselines. While varying across tasks, an improvement in accuracy was observed in the MKQA task, and the framework effectively optimizes the multi-objective reward function, even when minor trade-offs in other metrics like Accuracy and Contextual Relevance were observed in some contexts. Our framework enables robust, context-aware, and adaptive evaluation, laying a foundation for safer and more reliable LLM application in healthcare. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Status, Prospects and Future)
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Review

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28 pages, 835 KB  
Review
A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
by Harald H. Rietdijk, Patricia Conde-Cespedes, Talko B. Dijkhuis, Hilbrand K. E. Oldenhuis and Maria Trocan
Appl. Sci. 2025, 15(13), 7528; https://doi.org/10.3390/app15137528 - 4 Jul 2025
Cited by 2 | Viewed by 1696
Abstract
Human Digital Twins are an emerging type of Digital Twin used in healthcare to provide personalized support. Following this trend, we intend to elevate our virtual fitness coach, a coaching platform using wearable data on physical activity, to the level of a personalized [...] Read more.
Human Digital Twins are an emerging type of Digital Twin used in healthcare to provide personalized support. Following this trend, we intend to elevate our virtual fitness coach, a coaching platform using wearable data on physical activity, to the level of a personalized Human Digital Twin. Preliminary investigations revealed a significant difference in performance, as measured by prediction accuracy and F1-score, between the optimal choice of machine learning algorithms for generalized and personalized processing of the available data. Based on these findings, this survey aims to establish the state of the art in the selection and application of machine learning algorithms in Human Digital Twin applications in healthcare. The survey reveals that, unlike general machine learning applications, there is a limited body of literature on optimization and the application of meta-learning in personalized Human Digital Twin solutions. As a conclusion, we provide direction for further research, formulated in the following research question: how can the optimization of human data feature engineering and personalized model selection be achieved in Human Digital Twins and can techniques such as meta-learning be of use in this context? Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Status, Prospects and Future)
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