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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


BMC Medical Education called 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 sought to explore the diverse applications of AI in clinical reasoning education across various medical disciplines and educational settings.

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

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 called 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 sought to explore the diverse applications of AI in clinical reasoning education across various medical disciplines and educational settings. We invited contributions that delved into, but were 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.


Image credit: © [M] Parradee / stock.adobe.com

  2. To assess the ability of General Practice (GP) Trainees to detect AI-generated hallucinations in simulated clinical practice, ChatGPT-4o was utilized. The hallucinations were categorized into three types based...

    Authors: Jiacheng Zhou, Jintao Zhang, Rongrong Wan, Xiaochuan Cui, Qiyu Liu, Hua Guo, Xiaofen Shi, Bingbing Fu, Jia Meng, Bo Yue, Yunyun Zhang and Zhiyong Zhang
    Citation: BMC Medical Education 2025 25:406
    Content type: Research Published on:

  3. Pre-clerkship medical students benefit from practice questions that provide rationales for answer choices. Creating these rationales is a time-intensive endeavor. Therefore, not all practice multiple choice qu...

    Authors: Peter Y. Ch’en, Wesley Day, Ryan C. Pekson, Juan Barrientos, William B. Burton, Allison B. Ludwig, Sunit P. Jariwala and Todd Cassese
    Citation: BMC Medical Education 2025 25:333
    Content type: Research Published on:

  4. Artificial Intelligence is currently being applied in healthcare for diagnosis, decision-making and education. ChatGPT-4o, with its advanced language and problem-solving capabilities, offers an innovative alte...

    Authors: Selcen Öncü, Fulya Torun and Hilal Hatice Ülkü
    Citation: BMC Medical Education 2025 25:278
    Content type: Research Published on:

  5. The chatbot application Bennie and the Chats was introduced due to the outbreak of COVID-19, which is aimed to provide substitution for teaching conventional clinical history-taking skills. It was implemented wit...

    Authors: Kwan Yin Chan, Tsz Hon Yuen and Michael Co
    Citation: BMC Medical Education 2025 25:201
    Content type: Research Published on:

  7. Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. ...

    Authors: Nikhil Gupta, Kavin Khatri, Yogender Malik, Amit Lakhani, Abhinav Kanwal, Sameer Aggarwal and Anshul Dahuja
    Citation: BMC Medical Education 2024 24:1544
    Content type: Review Published on:

  8. Clinical decision-making (CDM) refers to physicians’ ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally ...

    Authors: Emilia Brügge, Sarah Ricchizzi, Malin Arenbeck, Marius Niklas Keller, Lina Schur, Walter Stummer, Markus Holling, Max Hao Lu and Dogus Darici
    Citation: BMC Medical Education 2024 24:1391
    Content type: Research Published on:

  9. This study aimed to evaluate the performance of GPT-3.5, GPT-4, GPT-4o and Google Bard on the United States Medical Licensing Examination (USMLE), the Professional and Linguistic Assessments Board (PLAB), the ...

    Authors: Yikai Chen, Xiujie Huang, Fangjie Yang, Haiming Lin, Haoyu Lin, Zhuoqun Zheng, Qifeng Liang, Jinhai Zhang and Xinxin Li
    Citation: BMC Medical Education 2024 24:1372
    Content type: Research Published on:

  10. Traditional puncture skills training for refresher doctors faces limitations in effectiveness and efficiency. This study explored the application of generative AI (ChatGPT), templates, and digital imaging to e...

    Authors: Zhe Ji, Yuliang Jiang, Haitao Sun, Bin Qiu, Yi Chen, Mao Li, Jinghong Fan and Junjie Wang
    Citation: BMC Medical Education 2024 24:1328
    Content type: Research Published on:

  11. The landscape of general surgery education has undergone a significant transformation over the past few years, driven in large part by the advent of surgical simulation and training technologies. These innovat...

    Authors: Aidin Shahrezaei, Maryam Sohani, Soroush Taherkhani and Seyed Yahya Zarghami
    Citation: BMC Medical Education 2024 24:1297
    Content type: Review Published on:

  12. Artificial intelligence (AI) is transforming health profession education (HPE) through personalized learning technologies. HPE students must also learn about AI to understand its impact on healthcare delivery....

    Authors: Wegdan Bani Issa, Ali Shorbagi, Alham Al-Sharman, Mohammad Rababa, Khalid Al-Majeed, Hadia Radwan, Fatma Refaat Ahmed, Nabeel Al-Yateem, Richard Mottershead, Dana N. Abdelrahim, Heba Hijazi, Wafa Khasawneh, Ibrahim Ali, Nada Abbas and Randa Fakhry
    Citation: BMC Medical Education 2024 24:1166
    Content type: Research Published on:

  13. Artificial intelligence (AI) chatbots have demonstrated proficiency in structured knowledge assessments; however, there is limited research on their performance in scenarios involving diagnostic uncertainty, w...

    Authors: Ryan S. Huang, Ali Benour, Joel Kemppainen and Fok-Han Leung
    Citation: BMC Medical Education 2024 24:1133
    Content type: Research Published on:

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.