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Improving Healthcare with Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2633

Special Issue Editors


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Guest Editor
Federal Institute of Maranhão, Campus Araioses, Araioses 65570-000, Brazil
Interests: machine/deep learning; natural language processing; computer vision; mobile/ubiquitous computing; serious games

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Guest Editor
Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil
Interests: health informatics; neuroinovation; electrophysiology; brain mapping

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) stands as a catalyst for revolutionizing the healthcare landscape, offering transformative solutions to longstanding challenges. This Special Issue dives into the myriad of ways AI is reshaping healthcare delivery, improving patient outcomes, and optimizing operational efficiency across various domains. The evolution of AI continues to unfold, showcasing its undeniable impact on healthcare.

The objective of this Special Issue is to furnish valuable insights into continuous progression, promising opportunities, and persistent challenges associated with harnessing AI's power to elevate healthcare delivery and elevate outcomes for patients globally. Primary studies and reviews related to AI model development/validation and experiments with AI-based systems are welcome for inclusion in this Special Issue.

Prof. Dr. Ariel Soares Teles
Dr. Silmar Teixeira
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 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. 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

  • AI-enabled healthcare innovations
  • AI-powered clinical decision support systems
  • predictive analytics for healthcare outcomes
  • AI-driven patient monitoring and telehealth
  • explainable AI in healthcare applications
  • virtual healthcare assistants and chatbots
  • AI-driven personalized patient care pathways
  • wearable/mobile devices and IoT integration with AI in healthcare
  • AI for mental health diagnosis and treatment
  • AI-driven medical education and training

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Published Papers (1 paper)

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37 pages, 11156 KiB  
Systematic Review
Machine Learning Methods in Student Mental Health Research: An Ethics-Centered Systematic Literature Review
by Mohamed Drira, Sana Ben Hassine, Michael Zhang and Steven Smith
Appl. Sci. 2024, 14(24), 11738; https://doi.org/10.3390/app142411738 - 16 Dec 2024
Viewed by 2251
Abstract
This study conducts an ethics-centered analysis of the AI/ML models used in Student Mental Health (SMH) research, considering the ethical principles of fairness, privacy, transparency, and interpretability. First, this paper surveys the AI/ML methods used in the extant SMH literature published between 2015 [...] Read more.
This study conducts an ethics-centered analysis of the AI/ML models used in Student Mental Health (SMH) research, considering the ethical principles of fairness, privacy, transparency, and interpretability. First, this paper surveys the AI/ML methods used in the extant SMH literature published between 2015 and 2024, as well as the main health outcomes, to inform future work in the SMH field. Then, it leverages advanced topic modeling techniques to depict the prevailing themes in the corpus. Finally, this study proposes novel measurable privacy, transparency (reporting and replicability), interpretability, and fairness metrics scores as a multi-dimensional integrative framework to evaluate the extent of ethics awareness and consideration in AI/ML-enabled SMH research. Findings show that (i) 65% of the surveyed papers disregard the privacy principle; (ii) 59% of the studies use black-box models resulting in low interpretability scores; and (iii) barely 18% of the papers provide demographic information about participants, indicating a limited consideration of the fairness principle. Nonetheless, the transparency principle is implemented at a satisfactory level with mean reporting and replicability scores of 80%. Overall, our results suggest a significant lack of awareness and consideration for the ethical principles of privacy, fairness, and interpretability in AI/ML-enabled SMH research. As AI/ML continues to expand in SMH, incorporating ethical considerations at every stage—from design to dissemination—is essential for producing ethically responsible and reliable research. Full article
(This article belongs to the Special Issue Improving Healthcare with Artificial Intelligence)
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