This archived Call for Papers closed on July 31, 2023 - published papers can be found in the section Theme Issue: ChatGPT and Generative Language Models in Medical Education.

Still want to submit? Manuscripts on this topic can always be submitted to JMIR Medical Education in the section on Artificial Intelligence (AI) in Medical Education. Additional related Calls for Papers or permanent themes in other JMIR Publications journals are also open for submission:

JMIR Nursing - Artificial Intelligence (AI) and ChatGPT in Nursing

JMIR Human Factors - Theme Issue on Artificial Intelligence (AI) and Human Factors—Towards Successful Application of AI in Health Care

JMIR Cardio - Generative and Multimodal Artificial Intelligence (AI) in Digital Cardiovascular Medicine

JMIR Mental Health - Theme Issue on “Responsible Design, Integration, and Use of Generative AI in Mental Health

JMIR Aging - Using AI Tools to Improve Health and Health Care for Older Adults

Asian/Pacific Island Nursing Journal - API Health and Nursing Care

Journal of Medical Internet Research - Generative Language Models Including ChatGPT

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JMIR Medical Education, a leading journal with emphasis on digital approaches to medical education, is inviting submissions on the use of generative language models and generative artificial intelligence (AI) in medical education. Generative AI can be used to create new content, including text, computer code, images, simulations, and videos, and is expected to fundamentally disrupt how content is produced.

ChatGPT (Chat Generative Pre-trained Transformer) is a generative language model tool launched by OpenAI on November 30, 2022, enabling the public to converse with a machine on a broad range of topics. Since its release, ChatGPT has stimulated widespread conversation and momentum across various fields, including medicine. Studies have also shown preliminary evidence that ChatGPT has promising applications across the clinical workflow [1]. In medical education, a recent study [2] found that ChatGPT was able to pass the United States Medical Licensing Examination (Step 1 and Step 2), performing at a level comparable to a third-year medical student. Moreover, ChatGPT and similar generative AI have specific applications within medical education, including clinical vignette generation [1,3] and communications training with AI virtual patients [4]; however, they also pose a number of challenges that universities need to carefully address [5].

Inspired by the recent success of ChatGPT, as well as its significant impact on academia and medical training and education, JMIR Medical Education announces a new theme issue, inviting original research studies, case studies, tutorials, and viewpoint articles on the use of generative language models and generative AI in medical education [6]. Beyond ChatGPT, exploration and evaluation of other powerful language models such as Google’s “Language Model for Dialogue Applications” (LaMDA/BERT) are also in scope, as are applications using generative AI to generate medical education assets such as images (eg, with Dall-E).

Submissions are invited on, but not limited to, the following topics:

The potential of generative language models and generative AI for medical education, including their use in teaching and learning for clinical decision-making and patient care
Role of generative language models and AI in enhancing the quality of medical education, including the use of simulations, virtual patients, and practice boards or medical qualification questions, and other forms of digital learning resources
Use of generative language models for automated feedback or evaluation of performance or competency in medical education
The development and evaluation of virtual patients generated by generative language models
Assessment of the quality of information and simulations generated by generative language models, as well as strategies for improving the quality through proper prompting and other approaches
Training medical students and health care professionals on AI, specifically on generative language models, including the development of curricula and instructional materials
Use of ChatGPT for patient and consumer education
Training medical students and health care professionals on the ways their future patients will interact with generative language models and AI
Ethical and legal issues related to the use of generative language models and AI in medical education, including issues related to data privacy, bias, and transparency.
Academic integrity issues and policies describing how medical schools allow or disallow the use of generative language models
Current trends in medical education related to generative AI, including perceptions, ideas, models, and pilots or use cases
Viewpoints on the future of medical education in the age of ChatGPT
Tutorials (eg, how-to articles) on incorporating generative language models or AI into medical education, which should also include at least preliminary evaluative information about the curricular innovation described in the tutorial

JMIR Medical Education welcomes submissions from researchers, educators, and practitioners in medicine, health care, computer science, and related fields. Submissions from a breadth of professionals at all career stages who are engaged in medical education are welcome for this theme issue. We encourage both empirical and theoretical submissions, including original research, systematic reviews, viewpoints, and tutorials. We also encourage submissions that address practical challenges and opportunities related to the use of generative language models and AI in medical education.

Prospective authors are encouraged to read “The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers” [6].

We will not accept articles that are solely written by ChatGPT itself. Please refer to our emerging editorial policy regarding the use of ChatGPT in article ideation or manuscript preparation and specifically on how to disclose the use of generative tools in the manuscript. If original ChatGPT-generated text is used, it should be presented as a quote, textbox, or figure. In particular, we ask to keep the complete original ChatGPT transcripts used on file and submit them as Multimedia Appendices (or supplementary material).

All submissions will undergo a rigorous peer-review process, and accepted articles will be published as part of a special issue on generative language models and AI in medical education.

Submission deadline: July 31, 2023 (closed - Manuscripts on this topic can still be submitted to JMIR Medical Education in the section on Artificial Intelligence (AI) in Medical Education)

Authors are encouraged to submit study protocols or grant proposals to JMIR Research Protocols before data acquisition to pre-register the study (Registered Reports - subsequent acceptance in one of the JMIR journals is then guaranteed).

Submissions not reviewed or accepted for publication in this JMIR theme issue may be offered cascading peer review or transfer to other JMIR journals, according to standard JMIR Publications policies.

High-quality original research more focused on clinical applications than medical education (for example, training and evaluation of new models with clinical data/text or research articles) may be submitted or transferred to the Artificial Intelligence section of our flagship journal Journal of Medical Internet Research; highly technical papers may be transferred to or submitted to JMIR Medical Informatics or JMIR Biomedical Engineering. Selected papers also may be in scope for JMIR AI [7]. Papers related to the quality of ChatGPT advice and its impact for public health may also be in scope for JMIR Infodemiology; papers focussing on the impact on participatory medicine and the patient-doctor relationship are also in scope for the Journal of Participatory Medicine. Early-stage formative work that informs the design of future interventions or research is in scope for JMIR Formative Research.

For more information, please contact the guest editors of this special issue:

Guest Editors

Kaushik P Venkatesh, MBA, MPH

Harvard Medical School, Boston MA, USA

kaushik_venkatesh@hms.harvard.edu

Maged N Kamel Boulos, MBBCh, MSc, PhD, FHEA, SMIEEE

Sun Yat-sen University, China

mnkboulos@ieee.org

Acknowledgements

The original call for papers was drafted by ChatGPT, transcript is available in The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers [6].

References

1. Rao A, Pang M, Kim J, Kamineni M, Lie W, Prasad AK, Landman A, Dreyer KJ, Succi MD. Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow. medRxiv. Preprint posted online 2023 Feb 26. doi: 10.1101/2023.02.21.23285886

2. Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, Chartash D. How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med Educ 2023 Feb 8;9:e45312. doi: 10.2196/45312 PMID: 36753318

3. Benoit JRA. ChatGPT for Clinical Vignette Generation, Revision, and Evaluation. medRxiv. Preprint posted online 2023 Feb 8. doi: 10.1101/2023.02.04.23285478

4. Shorey S, Ang E, Yap J, Ng ED, Lau ST, Chui CK. A Virtual Counseling Application Using Artificial Intelligence for Communication Skills Training in Nursing Education: Development Study. J Med Internet Res 2019 Oct 29;21(10):e14658. doi: 10.2196/14658 PMID: 31663857

5. Sinhaliz S, Burd L, Du Preez J. How ChatGPT Could Revolutionize Academia - The AI Chatbot Could Enhance Learning, But Also Creates Some Challenges. IEEE Spectrum. 2023 Feb 22. https://spectrum.ieee.org/how-chatgpt-could-revolutionize-academia

6. Eysenbach G. The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers. JMIR Med Educ 2023;9:e46885. doi: 10.2196/46885

This paper is in the following e-collection/theme issue:

Supporting Students With Electronic Health Record–Embedded Learning Aids: A Mixed-Methods Study

Supporting Students With Electronic Health Record–Embedded Learning Aids: A Mixed-Methods Study