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Accepted for/Published in: JMIR Medical Education

Date Submitted: Mar 28, 2022
Open Peer Review Period: Mar 28, 2022 - May 23, 2022
Date Accepted: Sep 12, 2022
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study

Liu DS, Sawyer J, Luna A, Aoun J, Wang J, Boachie L, Halabi S, Cheng X, Joe B

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study

JMIR Med Educ 2022;8(4):e38325

DOI: 10.2196/38325

PMID: 36269641

PMCID: 9636531

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

The Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed-Methods National Survey

  • David Shalom Liu; 
  • Jake Sawyer; 
  • Alexander Luna; 
  • Jihad Aoun; 
  • Janet Wang; 
  • Lord Boachie; 
  • Safwan Halabi; 
  • Xi Cheng; 
  • Bina Joe

ABSTRACT

Background:

Given the rapidity with which artificial intelligence (AI) is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of AI topics into undergraduate medical education. This is to prepare future physicians to better work together with AI technology. However, the first step to curriculum development is to survey the needs of the end-users. There has not been a study to determine which mediums and which topics are most preferred by US medical students to learn about the topic of AI in medicine.

Objective:

We aim to survey US medical students on the need and means to incorporate AI in undergraduate medical education to assist with future education initiatives.

Methods:

A mixed-methods survey was sent through Qualtrics to US medical students in May 2021. Likert scale questions first assessed various perceptions regarding AI in medicine. We also asked how many hours they would like to spend per week to learn about AI. Then, we asked respondents to choose which learning format and which AI topics they would be most interested in. Finally, we used a free-response section to capture any remaining thoughts.

Results:

A total of 390 US medical students (average age: 26±3) from 17 different medical programs were surveyed (estimated response rate: 3.5%). A majority (92%) of respondents agreed that training in AI concepts during medical school is useful for their future career, but 91% reported of receiving no formal education related to AI. While 79% are excited to use AI technologies, 91% reported that their medical schools did not offer resources. Short lectures (68%), formal electives (48%), and Q&A panels (44%) were identified as preferred formats, while fundamental concepts of AI (65%), when to use AI in medicine (60%) and pros and cons of using AI (59%) were the most preferred topics for enhancing their training. Responses for the preferred formats and topics significantly differed between respondents who answered they wanted to spend ≤2 hours vs. ≥3 hours per month to learn about AI.

Conclusions:

The results of this study indicate that current US medical students recognize the importance of AI in medicine, current formal education, and resources to learn AI-related topics are limited in most U.S. medical schools. Respondents indicated that a hybrid formal/ flexible format would be most appropriate for incorporating AI as a topic in US medical schools. Furthermore, multiple learning objectives for different groups of learners according to their future goals with AI (users or innovators) might be necessary.


 Citation

Please cite as:

Liu DS, Sawyer J, Luna A, Aoun J, Wang J, Boachie L, Halabi S, Cheng X, Joe B

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study

JMIR Med Educ 2022;8(4):e38325

DOI: 10.2196/38325

PMID: 36269641

PMCID: 9636531

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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