Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study

BMC Medical Education

Research

Natasha Caton¹  Fabio Y. Moraes²  Wonjae Lee³  Jacqueline Justino Nabhen⁴  Aidan Pucchio⁵  Raahulan Rathagirishnan⁵  Joshua Del Papa⁵  Peter J. Gariscsak⁵  Vicky Vo⁶ 

[1] Department of Medicine, University of British Columbia, 317 - 2194 Health Sciences Mall, V6T 1Z3, Vancouver, BC, Canada;Department of Oncology, Queen’s University, 25 King St W, K7L 5P9, Kingston, ON, Canada;Kingston Health Sciences Centre, 25 King St W, K7L 5P9, Kingston, ON, Canada;Michael G. DeGroote School of Medicine, McMaster University, 1280 Main Street West, Michael DeGroote Centre for Learning and Discovery – 3104, L8S 4K1, Hamilton, ON, Canada;School of Medicine, Federal University of Paraná, Rua XV de Novembro, 1299 - Centro, 80060-000, Curitiba, PR, Brazil;School of Medicine, Queen’s University, 15 Arch Street Kingston, K7L 3N6, Kingston, ON, Canada;Schulich School of Medicine & Dentistry, London, Ontario Canada Schulich School of Medicine & Dentistry, Western University, Clinical Skills Building, N6A 5C1, London, ON, Canada;

关键词: Artificial intelligence;    Curriculum;    Deep learning;    Education, medical;    Machine intelligence;    Machine learning;    Undergraduate;   

DOI  :  10.1186/s12909-022-03896-5

 received in 2022-07-25, accepted in 2022-11-15,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundEmerging artificial intelligence (AI) technologies have diverse applications in medicine. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study examines undergraduate medical students’ perceptions of AI, educational opportunities about of AI in medicine, and the desired medium for AI curriculum delivery.MethodsA 32 question survey for undergraduate medical students was distributed from May–October 2021 to students to all 17 Canadian medical schools. The survey assessed the currently available learning opportunities about AI, the perceived need for learning opportunities about AI, and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale survey questions were scored from 1 (disagree) to 5 (agree). Interview transcripts were analyzed using qualitative thematic analysis.ResultsWe received 486 responses from 17 of 17 medical schools (roughly 5% of Canadian undergraduate medical students). The mean age of respondents was 25.34, with 45% being in their first year of medical school, 27% in their 2nd year, 15% in their 3rd year, and 10% in their 4th year. Respondents agreed that AI applications in medicine would become common in the future (94% agree) and would improve medicine (84% agree Further, respondents agreed that they would need to use and understand AI during their medical careers (73% agree; 68% agree), and that AI should be formally taught in medical education (67% agree). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (85% disagree) and that AI-related learning opportunities were inadequate (74% disagree). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum.ConclusionA lack of educational opportunities about AI in medicine were identified across Canada in the participating students. As AI tools are currently progressing towards clinical implementation and there is currently a lack of educational opportunities about AI in medicine, AI should be considered for inclusion in formal medical curriculum.

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2023

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