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Artificial Intelligence in Healthcare: Opportunities and Challenges
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Artificial Intelligence in Healthcare: Opportunities and Challenges

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 13045

Special Issue Editor

Dr. Thomas Yuen Tung Lam

E-Mail Website
Guest Editor
The Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong, China
Interests: digital health and artificial intelligence in medicine; cancer and disease prevention; big data science; endoscopy nursing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) technologies in healthcare settings have grown rapidly in the past few years because of the exponential increase of computational power, the reduced cost of data storage, improved algorithmic sophistication, and the increased availability of health data from electronic health records. AI technologies have changed the how healthcare can be delivered in different clinical settings, such as disease diagnosis, risk prediction, and treatment management.

This Special Issue aims to present the most up-to-date data on the development and implementation of AI technologies in different healthcare settings. In this Special Issue, original research articles and reviews are welcome and research areas may include, but are not limited to, the following:

  • Systematic reviews and meta-analyses of existing AI medicine technologies;
  • Narrative reviews of existing AI medicine technologies;
  • Clinical trials to validate AI medicine technologies.

I look forward to receiving your contributions.

Dr. Thomas Yuen Tung Lam
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
  • big data
  • neural network model
  • natural language processing
  • chatbot
  • decision trees
  • healthcare
  • clinical trial
  • medicine

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Published Papers (6 papers)

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Research

Jump to: Review, Other

21 pages, 1277 KiB  
Article
HeartEnsembleNet: An Innovative Hybrid Ensemble Learning Approach for Cardiovascular Risk Prediction
by Syed Ali Jafar Zaidi, Attia Ghafoor, Jun Kim, Zeeshan Abbas and Seung Won Lee
Healthcare 2025, 13(5), 507; https://doi.org/10.3390/healthcare13050507 - 26 Feb 2025
Cited by 3 | Viewed by 693
Abstract
Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients [...] Read more.
Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients using various supervised, unsupervised, and deep learning approaches. Methods: This study presents HeartEnsembleNet, a novel hybrid ensemble learning model that integrates multiple machine learning (ML) classifiers for CVD risk assessment. The model is evaluated against six classical ML classifiers, including support vector machine (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), and random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) and ensemble techniques including stacking and voting. Results: Employing a dataset of 70,000 cardiac patients with 12 clinical attributes, our proposed model achieves a notable accuracy of 92.95% and a precision of 93.08%. Conclusions: These results highlight the effectiveness of hybrid ensemble learning in enhancing CVD risk prediction, offering a promising framework for clinical decision support. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)
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17 pages, 1647 KiB  
Article
Adoption of Artificial Intelligence in Rehabilitation: Perceptions, Knowledge, and Challenges Among Healthcare Providers
by Monira I. Aldhahi, Amal I. Alorainy, Mohamed M. Abuzaid, Awadia Gareeballah, Naifah F. Alsubaie, Anwar S. Alshamary and Zuhal Y. Hamd
Healthcare 2025, 13(4), 350; https://doi.org/10.3390/healthcare13040350 - 7 Feb 2025
Viewed by 1674
Abstract
Background/Objectives: The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness [...] Read more.
Background/Objectives: The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice. Methods: This study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables. Results: The majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training has commonly been reported, with social media platforms being respondents’ primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents. Conclusions: The results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI’s future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI’s potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)
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13 pages, 1004 KiB  
Article
Assessment Study of ChatGPT-3.5’s Performance on the Final Polish Medical Examination: Accuracy in Answering 980 Questions
by Julia Siebielec, Michal Ordak, Agata Oskroba, Anna Dworakowska and Magdalena Bujalska-Zadrozny
Healthcare 2024, 12(16), 1637; https://doi.org/10.3390/healthcare12161637 - 16 Aug 2024
Cited by 5 | Viewed by 1770
Abstract
Background/Objectives: The use of artificial intelligence (AI) in education is dynamically growing, and models such as ChatGPT show potential in enhancing medical education. In Poland, to obtain a medical diploma, candidates must pass the Medical Final Examination, which consists of 200 questions with [...] Read more.
Background/Objectives: The use of artificial intelligence (AI) in education is dynamically growing, and models such as ChatGPT show potential in enhancing medical education. In Poland, to obtain a medical diploma, candidates must pass the Medical Final Examination, which consists of 200 questions with one correct answer per question, is administered in Polish, and assesses students’ comprehensive medical knowledge and readiness for clinical practice. The aim of this study was to determine how ChatGPT-3.5 handles questions included in this exam. Methods: This study considered 980 questions from five examination sessions of the Medical Final Examination conducted by the Medical Examination Center in the years 2022–2024. The analysis included the field of medicine, the difficulty index of the questions, and their type, namely theoretical versus case-study questions. Results: The average correct answer rate achieved by ChatGPT for the five examination sessions hovered around 60% and was lower (p < 0.001) than the average score achieved by the examinees. The lowest percentage of correct answers was in hematology (42.1%), while the highest was in endocrinology (78.6%). The difficulty index of the questions showed a statistically significant correlation with the correctness of the answers (p = 0.04). Questions for which ChatGPT-3.5 provided incorrect answers had a lower (p < 0.001) percentage of correct responses. The type of questions analyzed did not significantly affect the correctness of the answers (p = 0.46). Conclusions: This study indicates that ChatGPT-3.5 can be an effective tool for assisting in passing the final medical exam, but the results should be interpreted cautiously. It is recommended to further verify the correctness of the answers using various AI tools. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)
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Review

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33 pages, 1620 KiB  
Review
Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration
by Syed Raza Abbas, Zeeshan Abbas, Arifa Zahir and Seung Won Lee
Healthcare 2024, 12(24), 2587; https://doi.org/10.3390/healthcare12242587 - 22 Dec 2024
Cited by 5 | Viewed by 4824
Abstract
Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL’s applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower [...] Read more.
Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL’s applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL’s limitations. Successfully addressing these hurdles is essential for enhancing FL’s efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)
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Other

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22 pages, 2814 KiB  
Systematic Review
Artificial Intelligence-Based Software as a Medical Device (AI-SaMD): A Systematic Review
by Shouki A. Ebad, Asma Alhashmi, Marwa Amara, Achraf Ben Miled and Muhammad Saqib
Healthcare 2025, 13(7), 817; https://doi.org/10.3390/healthcare13070817 - 3 Apr 2025
Viewed by 369
Abstract
Background/Objectives: Artificial intelligence-based software as a medical device (AI-SaMD) refers to AI-powered software used for medical purposes without being embedded in physical devices. Despite increasing approvals over the past decade, research in this domain—spanning technology, healthcare, and national security—remains limited. This research aims [...] Read more.
Background/Objectives: Artificial intelligence-based software as a medical device (AI-SaMD) refers to AI-powered software used for medical purposes without being embedded in physical devices. Despite increasing approvals over the past decade, research in this domain—spanning technology, healthcare, and national security—remains limited. This research aims to bridge the existing research gap in AI-SaMD by systematically reviewing the literature from the past decade, with the aim of classifying key findings, identifying critical challenges, and synthesizing insights related to technological, clinical, and regulatory aspects of AI-SaMD. Methods: A systematic literature review based on the PRISMA framework was performed to select the relevant AI-SaMD studies published between 2015 and 2024 in order to uncover key themes such as publication venues, geographical trends, key challenges, and research gaps. Results: Most studies focus on specialized clinical settings like radiology and ophthalmology rather than general clinical practice. Key challenges to implement AI-SaMD include regulatory issues (e.g., regulatory frameworks), AI malpractice (e.g., explainability and expert oversight), and data governance (e.g., privacy and data sharing). Existing research emphasizes the importance of (1) addressing the regulatory problems through the specific duties of regulatory authorities, (2) interdisciplinary collaboration, (3) clinician training, (4) the seamless integration of AI-SaMD with healthcare software systems (e.g., electronic health records), and (5) the rigorous validation of AI-SaMD models to ensure effective implementation. Conclusions: This study offers valuable insights for diverse stakeholders, emphasizing the need to move beyond theoretical analyses and prioritize practical, experimental research to advance the real-world application of AI-SaMDs. This study concludes by outlining future research directions and emphasizing the limitations of the predominantly theoretical approaches currently available. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)
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28 pages, 957 KiB  
Systematic Review
Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection
by Suhaylah Alkhalefah, Isra AlTuraiki and Najwa Altwaijry
Healthcare 2025, 13(6), 648; https://doi.org/10.3390/healthcare13060648 - 16 Mar 2025
Viewed by 868
Abstract
Background: Diabetic foot ulcers (DFUs) represent a significant challenge in managing diabetes, leading to higher patient complications and increased healthcare costs. Traditional approaches, such as manual wound assessment and diagnostic tool usage, often require significant resources, including skilled clinicians, specialized equipment, and [...] Read more.
Background: Diabetic foot ulcers (DFUs) represent a significant challenge in managing diabetes, leading to higher patient complications and increased healthcare costs. Traditional approaches, such as manual wound assessment and diagnostic tool usage, often require significant resources, including skilled clinicians, specialized equipment, and extensive time. Artificial intelligence (AI) and generative AI offer promising solutions for improving DFU management. This study systematically reviews the role of AI in DFU classification, prediction, segmentation, and detection. Furthermore, it highlights the role of generative AI in overcoming data scarcity and potential of AI-based smartphone applications for remote monitoring and diagnosis. Methods: A systematic literature review was conducted following the PRISMA guidelines. Relevant studies published between 2020 and 2025 were identified from databases including PubMed, IEEE Xplore, Scopus, and Web of Science. The review focused on AI and generative AI applications in DFU and excluded non-DFU-related medical imaging articles. Results: This study indicates that AI-powered models have significantly improved DFU classification accuracy, early detection, and predictive modeling. Generative AI techniques, such as GANs and diffusion models, have demonstrated potential in addressing dataset limitations by generating synthetic DFU images. Additionally, AI-powered smartphone applications provide cost-effective solutions for DFU monitoring, potentially improving diagnosis. Conclusions: AI and generative AI are transforming DFU management by enhancing diagnostic accuracy and predictive capabilities. Future research should prioritize explainable AI frameworks and diverse datasets for AI-driven healthcare solutions to facilitate broader clinical adoption. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare: Opportunities and Challenges)
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