AI & ICT in Healthcare

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

Deadline for manuscript submissions: 5 May 2027 | Viewed by 233

Editors


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Guest Editor
Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35233, USA
Interests: health informatics; knowledge representation and machine learning; applied operations research; public health and health services; simulation and modelling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Industrial and Manufacturing Engineering Department, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
Interests: artificial intelligence; natural language processing; human AI interaction; human behavior; health informatics; customer experience; human factors and ergonomics

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and information and communication technologies (ICTs) are rapidly transforming healthcare systems worldwide. ICTs—such as electronic health records, telemedicine platforms, mobile health applications, wearable devices, remote patient monitoring systems, and interoperable digital infrastructures—enable more connected, data-driven, and efficient care delivery. When integrated with advanced AI methods, these technologies offer significant potential to enhance clinical decision-making, improve diagnostic accuracy, optimize treatment planning, and support continuous patient monitoring. Furthermore, they play a critical role in improving healthcare operations, accessibility, and population health management.

We are pleased to invite you to contribute to this Special Issue, which focuses on advancing research at the intersection of AI- and ICT-enabled healthcare solutions.

This Special Issue aims to advance research on the development, implementation, evaluation, and real-world impact of AI- and ICT-enabled solutions in healthcare. It seeks contributions that address clinical, operational, and technological applications, emphasizing both innovation and practical implementation. The topic aligns closely with the journal’s scope by addressing cutting-edge digital health solutions that enhance healthcare delivery, improve patient outcomes, increase system efficiency, expand accessibility, and support data-driven decision-making in modern healthcare systems.

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

  • Clinical decision support and diagnostic applications, including AI-driven tools for early and accurate detection, diagnosis, and treatment support;
  • Patient risk prediction and care management, including predictive analytics for disease progression, treatment outcomes, hospital readmissions, emergency department use, and length of stay;
  • Clinical information management, including natural language processing and large language model applications for extracting insights from electronic health records, summarizing clinical notes, and enhancing patient–provider communication;
  • Digital care delivery and remote monitoring, including telemedicine, mobile health, wearable technologies, and real-time analytics for personalized and continuous care;
  • Healthcare operations and system optimization, including intelligent scheduling, resource allocation, workflow management, and administrative decision support;
  • Digital health infrastructure, including interoperable systems, secure data exchange, cybersecurity, privacy-preserving machine learning, and federated learning.

We look forward to receiving your contributions.

Dr. Abdulaziz Ahmed
Guest Editor

Dr. Duha Ali
Guest Editor Assistant

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-anonymized 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
  • digital health
  • telemedicine
  • predictive analytics
  • clinical decision support
  • remote monitoring
  • health informatics
  • natural language processing
  • healthcare optimization

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Published Papers (1 paper)

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Research

14 pages, 906 KB  
Article
Evaluation and Comparison of Large Language Model Responses to Frequently Asked Questions Regarding Patellofemoral Pain Syndrome: A Quality and Readability Assessment Study
by Oktay Polat, Berk Koncalıoğlu, Mert Gündoğdu and Emrecan Akgün
Healthcare 2026, 14(17), 2694; https://doi.org/10.3390/healthcare14172694 - 24 Aug 2026
Abstract
Background: Patellofemoral pain syndrome (PFPS) is a common cause of anterior knee pain, and patients increasingly use large language models (LLMs) to obtain general medical information. However, the quality, reliability, and readability of LLM-generated responses to patient-oriented questions regarding PFPS remain uncertain. This [...] Read more.
Background: Patellofemoral pain syndrome (PFPS) is a common cause of anterior knee pain, and patients increasingly use large language models (LLMs) to obtain general medical information. However, the quality, reliability, and readability of LLM-generated responses to patient-oriented questions regarding PFPS remain uncertain. This study aimed to compare responses generated by four widely used LLMs. Methods: Seventeen frequently asked questions regarding PFPS were identified through Google searches and adapted into lay language. The questions were submitted to OpenAI GPT-5, Google Gemini 2.5 Pro, xAI Grok 4, and DeepSeek-V3.2-Exp using a standardized patient scenario. A total of 68 question-specific responses were independently evaluated by four orthopedic surgeons using the DISCERN instrument. Inter-rater reliability was assessed using the intraclass correlation coefficient. Readability was evaluated using the Gunning Fog Index, Coleman–Liau Index, and Flesch Reading Ease Score. Between-model comparisons were performed using the Friedman test, followed by Bonferroni-adjusted pairwise analyses. Results: The omnibus Friedman test showed a significant between-model difference in DISCERN scores (p = 0.002). In Bonferroni-adjusted pairwise comparisons, GPT-5 had lower DISCERN scores than Gemini 2.5 Pro (adjusted p = 0.006), Grok 4 (adjusted p = 0.021), and DeepSeek-V3.2-Exp (adjusted p = 0.036), whereas no significant differences were observed among the other three models. However, the absolute differences were small, and the between-model difference was not significant in the sensitivity analysis using the median evaluator score (p = 0.381). Inter-rater agreement was moderate for GPT-5 and DeepSeek-V3.2-Exp but poor for Gemini 2.5 Pro and Grok 4. Readability differed significantly among the models across all three indices. DeepSeek-V3.2-Exp generally showed more favorable numerical readability values, whereas Grok 4 tended to produce more difficult text; however, no model was consistently superior across all readability measures. The median Gunning Fog and Coleman–Liau scores for all four models exceeded the commonly recommended sixth- to eighth-grade reading level for patient education. Conclusions: The evaluated LLMs showed small and method-dependent differences in DISCERN-based information quality and variable differences in readability. Their responses may supplement general patient education, but the findings should not be interpreted as evidence of factual accuracy, clinical safety, or suitability for individualized decision-making. LLM-generated information should be critically reviewed and should not replace assessment by a qualified healthcare professional. Full article
(This article belongs to the Special Issue AI & ICT in Healthcare)
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