Background Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. Objective We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. Methods A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. Results We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. Conclusions The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence–related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

The role of artificial intelligence (AI) in radiology has grown exponentially in the recent years. One of the primary worries by medical students is that AI will cause the roles of a radiologist to become automated and thus obsolete. Therefore, there is a greater hesitancy by medical students to choose radiology as a specialty. However, it is in this time of change that the specialty needs new thinkers and leaders. In this succinct viewpoint, 2 medical students involved in AI and 2 radiologists specializing in AI or clinical informatics posit that not only are these fears false, but the field of radiology will be transformed in such a way due to AI that there will be novel reasons to choose radiology. These new factors include greater impact on patient care, new space for innovation, interdisciplinary collaboration, increased patient contact, becoming master diagnosticians, and greater opportunity for global health initiatives, among others. Finally, since medical students view mentorship as a critical resource when deciding their career path, medical educators must also be cognizant of these changes and not give much credence to the prevalent fearmongering. As the field and practice of radiology continue to undergo significant change due to AI, it is urgent and necessary for the conversation to expand from expert to expert to expert to student. Medical students should be encouraged to choose radiology specifically because of the changes brought on by AI rather than being deterred by it.

UNSTRUCTURED The role of artificial intelligence (AI) in radiology has grown exponentially in the recent years. One of the primary worries by medical students is that AI will cause the roles of a radiologist to become automated and thus, obsolete. Therefore, there is a greater hesitance by medical students to choose radiology as a specialty. However, it is in this time of change that the specialty needs new thinkers and leaders. In this succinct article, two medical students involved in AI and two radiologists specializing in AI/clinical informatics argue that not only are these fears false, but that the field of radiology will be transformed in such a way due to AI that there will be novel reasons to choose radiology. These new factors include greater impact on patient care, new space for innovation, interdisciplinary collaboration, increased patient contact, becoming master diagnosticians, and greater opportunity for global health initiatives, among others. Finally, since medical students view mentorship as a critical resource when deciding their career path, medical educators must also be cognizant of these changes and not put much weight to the prevalent fearmongering. As the field and practice of radiology continue to undergo significant change due to AI, it is urgent and necessary for the conversation to switch from expert-to-expert to expert-to-student. Medical students should be encouraged to choose radiology specifically because of the changes brought on by AI, rather than be deterred by it.

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.