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Advancing Health Practice and Education Through Digital Health and Artificial...
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Advancing Health Practice and Education Through Digital Health and Artificial Intelligence

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1066

Special Issue Editor

Dr. Taehwan Park

E-Mail Website
Guest Editor
Pharmacy Administration and Public Health, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
Interests: health economics; pharmacoeconomics; pharmacoepidemiology; public health; outcomes research; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital health and artificial intelligence (AI) are revolutionizing healthcare and education, offering unprecedented opportunities to improve outcomes, enhance accessibility, and streamline processes. These technologies are transforming the delivery of care, enabling data-driven decision making, and empowering patients and providers. In the realm of education, AI-powered tools are personalizing learning experiences, fostering adaptive instruction, and equipping future healthcare professionals with the skills needed to navigate a technology-driven landscape.

This Special Issue aims to explore the critical role of digital health and AI in advancing health practice and education. By showcasing cutting-edge research, innovative applications, and real-world case studies, it seeks to highlight the transformative potential of these technologies while addressing challenges such as ethical considerations, data privacy, and accessibility.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: AI applications in clinical decision making and patient care; digital health tools for chronic disease management and prevention; integration of AI in health education and training programs; ethical implications and challenges of adopting AI in healthcare; innovations in telehealth and remote monitoring technologies; AI’s role in addressing health disparities and promoting equity; and the evaluation and implementation of digital health solutions in diverse settings.

We look forward to receiving your contributions.

Dr. Taehwan Park
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

  • digital health
  • artificial intelligence
  • health practice
  • AI in medical training
  • clinical decision making
  • data-driven decision making

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

Published Papers (3 papers)

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Research

14 pages, 1363 KiB  
Article
Predicting Ischemic Stroke Patients to Transfer for Endovascular Thrombectomy Using Machine Learning: A Case Study
by Noreen Kamal, Joon-Ho Han, Simone Alim, Behzad Taeb, Abhishek Devpura, Shadi Aljendi, Judah Goldstein, Patrick T. Fok, Michael D. Hill, Joe Naoum-Sawaya and Elena Adela Cora
Healthcare 2025, 13(12), 1435; https://doi.org/10.3390/healthcare13121435 - 16 Jun 2025
Viewed by 136
Abstract
Introduction: Endovascular thrombectomy (EVT) is highly effective for ischemic stroke patients with a large vessel occlusion. EVT is typically only offered at urban hospitals; therefore, patients are transferred for EVT from hospitals that solely offer thrombolysis. There is uncertainly around patient selection [...] Read more.
Introduction: Endovascular thrombectomy (EVT) is highly effective for ischemic stroke patients with a large vessel occlusion. EVT is typically only offered at urban hospitals; therefore, patients are transferred for EVT from hospitals that solely offer thrombolysis. There is uncertainly around patient selection for transfer, which results in a large number of futile transfers. Machine learning (ML) may be able to provide a model that better predicts patients to transfer for EVT. Objective: The objective of the study is to determine if ML can provide decision support to more accurately select patients to transfer for EVT. Methods: This is a retrospective study. Data from Nova Scotia, Canada from 1 January 2018 to 31 December 2022 was used. Four supervised binary classification ML algorithms were applied, as follows: logistic regression, decision tree, random forest, and support vector machine. We also applied an ensemble method using the results of these four classification algorithms. The data was split into 80% training and 20% testing, and five-fold cross-validation was employed. Missing data was accounted for by the k-nearest neighbour’s algorithm. Model performance was assessed using accuracy, the futile transfer rate, and the false negative rate. Results: A total of 5156 ischemic stroke patients were identified during the time period. After exclusions, a final dataset of 93 patients was obtained. The accuracy of logistic regression, decision tree, random forest, support vector machine, and ensemble models was 68%, 79%, 74%, 63%, and 68%, respectively. The futile transfer rate with random forest and decision tree was 0% and 18.9%, respectively, and the false negative rate was 5.37 and 4.3%, respectively Conclusions: ML models can potentially reduce futile transfer rates, but future studies with larger datasets are needed to validate this finding and generalize it to other systems. Full article
(This article belongs to the Special Issue Advancing Health Practice and Education Through Digital Health and Artificial Intelligence)
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12 pages, 950 KiB  
Article
Evaluating the Reliability and Quality of Sarcoidosis-Related Information Provided by AI Chatbots
by Nur Aleyna Yetkin, Burcu Baran, Bilal Rabahoğlu, Nuri Tutar and İnci Gülmez
Healthcare 2025, 13(11), 1344; https://doi.org/10.3390/healthcare13111344 - 5 Jun 2025
Viewed by 289
Abstract
Background and Objectives: Artificial intelligence (AI) chatbots are increasingly employed for the dissemination of health information; however, apprehensions regarding their accuracy and reliability remain. The intricacy of sarcoidosis may lead to misinformation and omissions that affect patient comprehension. This study assessed the usability [...] Read more.
Background and Objectives: Artificial intelligence (AI) chatbots are increasingly employed for the dissemination of health information; however, apprehensions regarding their accuracy and reliability remain. The intricacy of sarcoidosis may lead to misinformation and omissions that affect patient comprehension. This study assessed the usability of AI-generated information on sarcoidosis by evaluating the quality, reliability, readability, understandability, and actionability of chatbot responses to patient-centered queries. Methods: This cross-sectional evaluation included 11 AI chatbots comprising both general-purpose and retrieval-augmented tools. Four sarcoidosis-related queries derived from Google Trends were submitted to each chatbot under standardized conditions. Responses were independently evaluated by four blinded pulmonology experts using DISCERN, the Patient Education Materials Assessment Tool—Printable (PEMAT-P), and Flesch–Kincaid readability metrics. A Web Resource Rating (WRR) score was also calculated. Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Results: Retrieval-augmented models such as ChatGPT-4o Deep Research, Perplexity Research, and Grok3 Deep Search outperformed general-purpose chatbots across the DISCERN, PEMAT-P, and WRR metrics. However, these high-performing models also produced text at significantly higher reading levels (Flesch–Kincaid Grade Level > 16), reducing accessibility. Actionability scores were consistently lower than understandability scores across all models. The ICCs exceeded 0.80 for all evaluation domains, indicating excellent inter-rater reliability. Conclusions: Although some AI chatbots can generate accurate and well-structured responses to sarcoidosis-related questions, their limited readability and low actionability present barriers for effective patient education. Optimization strategies, such as prompt refinement, health literacy adaptation, and domain-specific model development, are required to improve the utility of AI chatbots in complex disease communication. Full article
(This article belongs to the Special Issue Advancing Health Practice and Education Through Digital Health and Artificial Intelligence)
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11 pages, 203 KiB  
Article
Integrating AI in Healthcare Education: Attitudes of Pharmacy Students at King Khalid University Towards Using ChatGPT in Clinical Decision-Making
by Rajalakshimi Vasudevan, Taha Alqahtani, Saud Alqahtani, Praveen Devanandan, Geetha Kandasamy, Reema Saad, Asayel Amer, Raghad Abduallah, Ghada Waleed, Rahaf Hasan and Lamis Ahmed
Healthcare 2025, 13(11), 1265; https://doi.org/10.3390/healthcare13111265 - 27 May 2025
Viewed by 442
Abstract
Background: Artificial Intelligence (AI) is transforming healthcare education, offering innovative tools to enhance learning and clinical decision-making. AI-driven platforms like ChatGPT are increasingly integrated into pharmacy education, enabling students to access vast medical knowledge, refine decision-making skills, and simulate clinical scenarios. Objective: This [...] Read more.
Background: Artificial Intelligence (AI) is transforming healthcare education, offering innovative tools to enhance learning and clinical decision-making. AI-driven platforms like ChatGPT are increasingly integrated into pharmacy education, enabling students to access vast medical knowledge, refine decision-making skills, and simulate clinical scenarios. Objective: This study examines pharmacy students’ attitudes, knowledge, and practices regarding ChatGPT’s use in clinical decision-making, evaluates its perceived benefits and limitations, and identifies factors influencing AI integration in pharmacy education. Methodology: A cross-sectional study was conducted among 512 pharmacy students at King Khalid University. A structured questionnaire assessed demographics, knowledge, attitudes, and practices. Data were analyzed using SPSS, employing descriptive statistics, chi-square tests, and logistic regression. Results: The majority (82.4%) supported AI integration in pharmacy education, while 74.6% believed that ChatGPT could enhance clinical decision-making. Primary applications included drug information retrieval (72.3%) and exam preparation (66.7%). However, concerns about AI accuracy (55.2%) and ethical implications were noted. Conclusions: Pharmacy students at King Khalid University exhibit positive attitudes toward AI, recognizing its educational benefits while acknowledging challenges. Addressing accuracy concerns and ethical considerations through structured AI training programs is essential to optimize AI’s role in pharmacy education and practice. Full article
(This article belongs to the Special Issue Advancing Health Practice and Education Through Digital Health and Artificial Intelligence)
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