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

被引:27
作者
Pucchio, Aidan [1 ]
Rathagirishnan, Raahulan [1 ]
Caton, Natasha [2 ]
Gariscsak, Peter J. [1 ]
Del Papa, Joshua [1 ]
Nabhen, Jacqueline Justino [3 ]
Vo, Vicky [4 ]
Lee, Wonjae [5 ]
Moraes, Fabio Y. [6 ,7 ]
机构
[1] Queens Univ, Sch Med, 15 Arch St Kingston, Kingston, ON K7L 3N6, Canada
[2] Univ British Columbia, Dept Med, 317-2194 Hlth Sci Mall, Vancouver, BC V6T IZ3, Canada
[3] Univ Fed Parana, Sch Med, Rua XV Novembro,1299 Ctr, BR-80060000 Curitiba, PR, Brazil
[4] Western Univ, Schulich Sch Med & Dent, Clin Skills Bldg, London, ON N6A 5C1, Canada
[5] McMaster Univ, Michael G DeGroote Sch Med, Michael DeGroote Ctr Learning & Discovery, 1280 Main St West,3104, Hamilton, ON L8S 4K1, Canada
[6] Queens Univ, Dept Oncol, 25 King St W, Kingston, ON K7L 5P9, Canada
[7] Kingston Hlth Sci Ctr, 25 King St W, Kingston, ON K7L 5P9, Canada
关键词
Artificial intelligence; Curriculum; Deep learning; Education; medical; Machine intelligence; Machine learning; Undergraduate;
D O I
10.1186/s12909-022-03896-5
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Background Emerging 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. Methods A 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. Results We 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. Conclusion A 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.
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页数:12
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共 24 条
  • [11] Kocabas S., 2021, MCGILL J MED, V19, P1, DOI [10.26443/mjm.v19i1.871, DOI 10.26443/MJM.V19I1.871]
  • [12] Paranjape Ketan, 2019, JMIR Med Educ, V5, pe16048, DOI 10.2196/16048
  • [13] Artificial intelligence in the medical profession: ready or not, here AI comes
    Pucchio, Aidan
    Del Papa, Joshua
    de Moraes, Fabio Ynoe
    [J]. CLINICS, 2022, 77
  • [14] Medical students need artificial intelligence and machine learning training
    Pucchio, Aidan
    Eisenhauer, Elizabeth A.
    Moraes, Fabio Ynoe
    [J]. NATURE BIOTECHNOLOGY, 2021, 39 (03) : 388 - 389
  • [15] Reznick RK., 2020, R COLL PHYS SURG CAN, P1
  • [16] A Consensus-Based Checklist for Reporting of Survey Studies (CROSS)
    Sharma, Akash
    Minh Duc, Nguyen Tran
    Luu Lam Thang, Tai
    Nam, Nguyen Hai
    Ng, Sze Jia
    Abbas, Kirellos Said
    Huy, Nguyen Tien
    Marusic, Ana
    Paul, Christine L.
    Kwok, Janette
    Karbwang, Juntra
    de Waure, Chiara
    Drummond, Frances J.
    Kizawa, Yoshiyuki
    Taal, Erik
    Vermeulen, Joeri
    Lee, Gillian H. M.
    Gyedu, Adam
    To, Kien Gia
    Verra, Martin L.
    Jacqz-Aigrain, Evelyne M.
    Leclercq, Wouter K. G.
    Salminen, Simo T.
    Sherbourne, Cathy Donald
    Mintzes, Barbara
    Lozano, Sergi
    Tran, Ulrich S.
    Matsui, Mitsuaki
    Karamouzian, Mohammad
    [J]. JOURNAL OF GENERAL INTERNAL MEDICINE, 2021, 36 (10) : 3179 - 3187
  • [17] Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada
    Teng, Minnie
    Singla, Rohit
    Yau, Olivia
    Lamoureux, Daniel
    Gupta, Aurinjoy
    Hu, Zoe
    Hu, Ricky
    Aissiou, Amira
    Eaton, Shane
    Hamm, Camille
    Hu, Sophie
    Kelly, Dayton
    MacMillan, Kathleen M.
    Malik, Shamir
    Mazzoli, Vienna
    Teng, Yu-Wen
    Laricheva, Maria
    Jarus, Tal
    Field, Thalia S.
    [J]. JMIR MEDICAL EDUCATION, 2022, 8 (01):
  • [18] The Association of Faculties of Medicine of Canada, 2019, CAN MED ED STAT 2019
  • [19] Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups
    Tong, Allison
    Sainsbury, Peter
    Craig, Jonathan
    [J]. INTERNATIONAL JOURNAL FOR QUALITY IN HEALTH CARE, 2007, 19 (06) : 349 - 357
  • [20] High-performance medicine: the convergence of human and artificial intelligence
    Topol, Eric J.
    [J]. NATURE MEDICINE, 2019, 25 (01) : 44 - 56