Call for papers - Artificial intelligence in clinical reasoning education

Guest Editors

Yew Kong Lee, PhD, Universiti Malaya, Malaysia
Alexandre Sampaio Moura, MD, MPH, PhD, Faculdade Santa Casa BH, Brazil
Andrew S. Parsons, MD, MPH, FACP, University of Virginia School of Medicine, USA

Submission Status: Open | Submission Deadline: 20 January 2025

BMC Medical Education is calling for submissions to its Collection on Artificial intelligence in clinical reasoning education. Clinical reasoning, the process by which healthcare professionals gather and analyze patient information to make diagnostic and therapeutic decisions, lies at the heart of effective medical practice. Traditional methods of teaching clinical reasoning often rely on experiential learning, case-based discussions, and mentorship. However, the incorporation of AI offers unprecedented opportunities to enhance and revolutionize this fundamental aspect of medical education. This Collection seeks to explore the diverse applications of AI in clinical reasoning education across various medical disciplines and educational settings.

New Content Item This Collection supports and amplifies research related to SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities.

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Meet the Guest Editors

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Yew Kong Lee, PhD, Universiti Malaya, Malaysia

Dr Lee is a Senior Lecturer at the Department of Primary Care Medicine, University of Malaya, and Head of the eLearning portfolio at the Faculty of Medicine’s UMeHealth Unit. His research interests include the influence of patient values on health decisions, and patient-centered care in low resource settings. He has worked across a broad range of health topics including diabetes, cancer, genetics, HIV/AIDS, indigenous health in Sabah and Sarawak, and migrant worker health. He leads several eLearning and eHealth projects including MyViP@UM (virtual patients for medical education) and VISIT (a randomized trial to elicit patient concerns through electronic medical records).

Alexandre Sampaio Moura, MD, MPH, PhD, Faculdade Santa Casa BH, Brazil

Dr Moura is a professor and researcher at the Postgraduate Program in Medical and Biomedical Sciences at Faculdade Santa Casa BH. His research on Health Professions Education is focused on clinical reasoning, professionalism, and competence-based assessment.

Andrew S. Parsons, MD, MPH, FACP, University of Virginia School of Medicine, USA

Dr Parsons is an associate professor of medicine and practices as an internal medicine hospitalist at the University of Virginia (UVA). As Associate Dean for Clinical Competency for UVA School of Medicine, he oversees the teaching, assessment, and remediation of clinical skills across the four-year medical student curriculum. This includes oversight of competency-based faculty development and assessment programs. Within UVA Hospital Medicine, he is Associate Division Head for Research and Scholarship. His research is focused on coaching and remediation of clinical reasoning, specifically management reasoning.

About the Collection

BMC Medical Education is calling for submissions to its Collection on Artificial intelligence in clinical reasoning education.

Clinical reasoning, the process by which healthcare professionals gather and analyze patient information to make diagnostic and therapeutic decisions, lies at the heart of effective medical practice. Traditional methods of teaching clinical reasoning often rely on experiential learning, case-based discussions, and mentorship. However, the incorporation of AI offers unprecedented opportunities to enhance and revolutionize this fundamental aspect of medical education.

This Collection seeks to explore the diverse applications of AI in clinical reasoning education across various medical disciplines and educational settings. We invite contributions that delve into, but are not limited to, the following themes:

AI-enhanced diagnostic reasoning: Investigations into the use of AI algorithms, machine learning, and natural language processing to augment diagnostic reasoning skills among medical students, residents, and practicing clinicians.
Virtual patient simulations: Studies examining the efficacy of AI-driven virtual patient simulations in providing learners with realistic clinical scenarios for honing diagnostic and therapeutic decision-making abilities.
Personalized learning platforms: Exploration of AI-powered adaptive learning platforms tailored to individual learner needs, preferences, and proficiency levels in clinical reasoning.
Ethical considerations: Discussions on the ethical implications of integrating AI technologies into clinical reasoning education, including issues related to bias, privacy, and accountability.
Comparative effectiveness: Comparative studies evaluating the effectiveness of AI-based approaches versus traditional methods in fostering clinical reasoning skills and improving patient outcomes.
Interdisciplinary perspectives: Collaborative research efforts that bridge the gap between AI specialists, medical educators, cognitive psychologists, and healthcare practitioners to advance our understanding of how AI can optimize clinical reasoning education.

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection; please select "Artificial intelligence in clinical reasoning education" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.