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Cited by 138Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

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

Open access
Pucchio, Aidan, Rathagirishnan, Raahulan, Caton, Natasha, Gariscsak, Peter J., Del Papa, Joshua, Nabhen, Jacqueline Justino, Vo, Vicky, Lee, Wonjae +1 more·2022·BMC Medical Education
DOI 10.1186/s12909-022-03896-5PMC PMC9703803
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Abstract

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.

AI snapshot

Written from the paper’s full text.

TL;DR

In a Canadian cross-sectional mixed-methods study, most surveyed undergraduate medical students believed AI would become common and improve medicine and wanted formal AI teaching, but most reported no formal AI education. Interviews echoed a lack of formal opportunities and concern that non-AI content would take curricular priority.

Key findings

  • Among 475 included survey respondents from all 17 Canadian medical schools, 94% agreed AI applications in medicine would become common, 84% agreed AI would improve medicine, 73% agreed they would need to use AI, 68% agreed they would need to understand AI, and 67% agreed AI should be formally taught in medical education.
  • 85% disagreed they had received formal curriculum training in AI, 74% disagreed AI-related learning opportunities were adequate, and 75.8% (360) reported no formal teaching about AI during medical school. Preferred formats were workshops (27.1%, 299), lectures (23.8%, 263), and collaborative activities with other departments such as computer science (17.3%, 191).
  • Interviews yielded three major themes: a lack of existing learning opportunities about AI, the need to incorporate AI learning in medical curriculum, and positive sentiment about the future of AI in medicine. Subthemes included elective/informal learning, scarcity of formal opportunities, desired formats, barriers to curriculum development, excitement, and fear about misuse or poor stewardship.
  • Students showed some cognitive dissonance: 74% agreed AI would revolutionize medicine, but only 31.7% agreed AI would particularly affect their specialty of choice while 35.1% disagreed; 66% disagreed that some or all physicians would be replaced by AI.

Methods

  • Cross-sectional mixed-methods study with a 56-question online survey (abstract says 32 questions) distributed May–October 2021 to all 17 Canadian medical schools; inclusion required consent and enrollment in a Canadian medical school at any time in 2021. There were 486 responses, of which 475 met inclusion criteria. Likert items were scored 1 (disagree) to 5 (agree).
  • Follow-up 10–15 minute interviews were conducted via Zoom between October and December 2021 with a stratified random sample from English-speaking medical schools; the paper reports 17 undergraduate medical students recruited from 11 medical schools, while the abstract states 18 students were interviewed.
  • Quantitative data were analyzed descriptively in SPSS 27.0, with Likert results reported as percentages agreeing/disagreeing and counts; interview transcripts were analyzed using emergent thematic analysis with two independent blinded coders, and inter-coder reliability was 82%.

Limitations

  • The paper states non-response and participant bias; respondents made up only ~4.5% of the total Canadian undergraduate medical student population, dissemination varied by school, later-stage and male respondents were underrepresented, no formal survey validation was employed beyond an internal pilot, the survey was lengthy, and the authors could not comment on how many participants were recruited through each medium.
  • The paper states participant bias likely affected outcomes, with respondents more likely to possess interest or knowledge of AI and stronger technical understanding than non-respondents.
  • Evident in the text: the abstract reports 18 interviews while the methods/results report 17 recruited, and the abstract describes a 32-question survey while the methods describe a 56-question survey.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s12909-022-03896-5

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 40 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Workshops were a preferred learning format about AI in medicine (27.1%, 299).

    “The preferred learning format about AI in medicine were workshops (27.1%, 299), lectures (23.8%, 263), and collaborative activities with other departments such as computer science (17.3%, 191).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Surveyed medical students agreed that their understanding of programming or mathematics was a barrier to understanding AI.

    “Students agreed that their understanding of programming or mathematics were a barrier to understanding AI (47% agree; 70 strongly agree, 141 agree).”
    Quote in the paperNot a substantial claim
  • Findingown data

    63% of respondents did not understand AI research methods.

    “Only 39% (34 strongly agree, 148 agree) of respondents were able to describe AI, machine learning, neural networks, and deep learning and 63% did not understand AI research methods.”
    Quote in the paperSubstantial claim
  • Findingown data

    Respondents agreed that they would use applications of AI during their careers (72.9% agree).

    “Medical students were unsure if AI would particularly affect their specialty of choice (31.7% agree, 35.1% disagree), but agreed that they will need to understand AI throughout their career (68.3% agree; 104 strongly agree, 212 agree) and that they would use applications of AI during their careers (72.9% agree; 110 strongly agree, 223 agree).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Respondents agreed that they will need to understand AI throughout their career (68.3% agree).

    “Medical students were unsure if AI would particularly affect their specialty of choice (31.7% agree, 35.1% disagree), but agreed that they will need to understand AI throughout their career (68.3% agree; 104 strongly agree, 212 agree) and that they would use applications of AI during their careers (72.9% agree; 110 strongly agree, 223 agree).”
    Quote in the paperNot a substantial claim
  • May combine two claims. 28
  • Classified as probably not a substantial claim. 30

  • May combine two claims.

    Findingown data

    Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum.

    “Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Qualitative analysis identified subthemes including the value of elective or informal learning, a scarcity of formal learning opportunities, desired formats of future AI education, barriers to development of AI curriculum, excitement about the future of AI in medicine, fear about misuse or poor stewardship of AI, and specific uses for AI including use in specific disciplines.

    “Subthemes that emerged included the value of elective or informal learning about AI, a scarcity of formal learning opportunities, the desired formats of future AI education, barriers to development of AI curriculum, excitement about the future of AI in medicine, fear about misuse or poor stewardship of AI, and specific uses for AI including use in specific disciplines.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The qualitative themes are transferable to the broader Canadian medical student population.

  • May combine two claims.

    Findingown data

    A small number of interviewees identified barriers to inclusion of AI in medical curriculum, notably that AI should not take priority over other missing topics.

    “Barriers to inclusion of AI in medical curriculum was established by a small number of participants. Notably, many participants noted that AI shouldn’t take priority over other missing topics in curriculum.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Interviewees thought a lack of technical understanding of mathematics, programming, and computer science were barriers to learning about AI.

    “A lack of technical understanding of mathematics, programming, and computer science were also thought to be barriers to learning about AI”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Qualitative analysis identified three major themes: a lack of existing learning opportunities about AI, the need to incorporate AI learning in medical curriculum, and positive sentiment about the future of AI in medicine.

    “Three major themes emerged in the qualitative analysis: a lack of existing learning opportunities about AI, the need to incorporate AI learning in medical curriculum, and positive sentiment about the future of AI in medicine.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The qualitative themes are transferable to the broader Canadian medical student population.

  • May combine two claims.

    Findingown data

    No interviewee had directly encountered the barrier of curricular prioritization; it was speculated that curricular prioritization would be an issue.

    “Notably, no student had directly encountered this barrier, but rather it was speculated that curricular prioritization would be an issue.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    In total, 486 responses were obtained and 475 met the inclusion criteria.

    “In total, 486 responses were obtained and 475 met the inclusion criteria, with survey respondent demographics outlined in Table 1.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The majority of interviewees noted the importance of learning about AI in medicine despite a lack of formal learning opportunities.

    “Despite a lack of formal learning opportunities, the importance of learning about AI in medicine developed, was noted by the majority of participants.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Some surveyed medical students had independently learned about AI, with agreement and disagreement nearly equal.

    “Some students had independently learned about AI (41.9% agree, 42.4% disagree).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Most interviewees believed inclusion of AI in curriculum was important, while a small number indicated it should not be a priority.

    “While most participants did believe inclusion in curriculum was important, a small number of participants indicated that it should not be a priority.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Respondents believed that AI will revolutionize medicine in the future (74% agree).

    “Student perceptions of AI in medicine included: the belief that AI has improved medicine (74% agree; 102 strongly agree, 234 agree), that AI is commonly used in medicine (M59% agree; 54 strongly agree, 217 agree), and that AI will revolutionize medicine in the future (74% agree; 150 strongly agree, 186 agree).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Interviewees expressed varying sentiments about AI, with some excitement about the future and others concern or fear about AI or misuse of AI.

    “Varying sentiments about AI were expressed by participants, with some references to excitement around the future of AI while other participants expressed concern or fear about AI or misuse of AI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Some interviewees thought there would be applications for patient use, such as symptom checkers.

    “Some participants also thought there would be applications for patient use, such as “symptom checkers, [where the patient can] input the symptoms, and it spits out possible diagnoses.””
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Survey respondents did not believe technical knowledge would be a barrier to uptake of AI, while interview participants expressed concern that a lack of mathematical or computer science knowledge would prevent effective learning about AI.

    “Although survey respondents did not believe technical knowledge would be a barrier to uptake of AI, interview participants did express concern about a lack of mathematical or computer science knowledge prevent effective learning about AI.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Interviewee concerns about AI included ethical issues, accuracy of AI tools, and job security.

    “There were a variety of concerns brought up by participants, including “… concerns regarding the ethical issues surrounding AI” or the accuracy of AI tools. While infrequently noted, there was some concern about job security; “maybe there’ll be less jobs for physicians in certain fields that AI is more applicable to, like radiology or pathology.””
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Concrete skills about how to interpret and use AI and integrate AI tools into clinical workflow may become important for physicians in the near future.

    “Concrete skills about how to interpret and use AI and integrate AI tools into clinical workflow may become important for physicians in the near future [3].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [3] Pucchio A, Eisenhauer EA, Moraes FY. Medical students need artificial intelligence and machine learning training. Nat Biotechnol. 2021;39(3):388–9 Available from: http://www.nature.com/articles/s41587-021-00846-2. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Professional and regulatory bodies have begun to recognize the value of AI as a core competency for physicians, as demonstrated by the Canadian Royal College Task Force Report.

    “Professional and regulatory bodies have also begun to recognize the value of AI as a core competency for physicians, as is demonstrated by the recent establishment of the Canadian Royal College of Physicians and Surgeons Task Force Report on Artificial Intelligence and Emerging Digital Technologies [6].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Reznick RK, Harris K, Horsley T, Hassani MS. Task Force Report on Artificial Intelligence and Emerging Digital Technologies. R Coll Physicians Surg Canada. 2020;(February):1–52. Available from: http://www.royalcollege.ca/rcsite/health-policy/initiatives/ai-task-force-e.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    Other key skills in medicine such as ethical aspects of decision making or humanization and empathy through doctor-patient interaction may become more important.

    “Additionally, other key skills in medicine such as the ethical aspects of decision making or humanization and empathy through the doctor–patient interaction may become more important [3–5].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [3] Pucchio A, Eisenhauer EA, Moraes FY. Medical students need artificial intelligence and machine learning training. Nat Biotechnol. 2021;39(3):388–9 Available from: http://www.nature.com/articles/s41587-021-00846-2. source

      Not checked in the free run.

    • cites [4] Han E-R, Yeo S, Kim M-J, Lee Y-H, Park K-H, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Med Educ. 2019;19(1):460 Available from: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-019-1891-5. source

      Not checked in the free run.

    • cites [5] Chin-Yee B, Upshur R. Clinical judgement in the era of big data and predictive analytics. J Eval Clin Pract. 2018;24(3):638–45. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    It is plausible that all of the ~10,000 Canadian undergraduate medical students had the opportunity to respond.

    “While it is impossible to know how many students received the survey given the variable methods of distribution, it is plausible that all of the ~ 10,000 Canadian undergraduate medical students had the opportunity to respond [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] The Association of Faculties of Medicine of Canada. Canadian Medical Education Statistics 2019. 2019. Available from: https://www.afmc.ca/sites/default/files/pdf/CMES/CMES2019-Complete_EN.pdf. [Cited 2022 Feb 17].

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Finalization of themes occurred after review and discussion by the two coders.

    “Finalization of themes occurred after review and discussion by the two coders.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    No follow-up questions were asked, the interviewers provided no additional information during the interviews, and there was no repeat questioning at a later date.

    “No follow-up questions were asked, the interviewers provided no additional information during the interviews, and there was no repeat questioning at a later date.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Two independent, blinded coders performed emergent coding.

    “Two independent, blinded coders (AP, NC) performed emergent coding consisting of assigning all phrases of the interview data into codes, regardless of their expected relevance to future themes [14, 15].”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    All participants were familiar with the research goals.

    “All participants were familiar with the research goals.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The inter-coder reliability was 82%.

    “The inter-coder reliability was 82%.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    Members of the interview and research team had previously expressed that they believe AI training in medicine is important in their published work.

    “Members of the interview and research team had previously expressed that they believe AI training in medicine in important in their published work [3].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [3] Pucchio A, Eisenhauer EA, Moraes FY. Medical students need artificial intelligence and machine learning training. Nat Biotechnol. 2021;39(3):388–9 Available from: http://www.nature.com/articles/s41587-021-00846-2. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Previous studies have found that undergraduate medical students believe AI will be an integral part of medical practice in the future.

    “Previous studies have addressed the perceptions of undergraduate medical students about AI in medicine, noting that they believe AI will be an integral part of medical practise in the future [7, 8].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [7] Teng M, Singla R, Yau O, Lamoureux D, Gupta A, Hu Z, et al. Health care students’ perspectives on Artificial intelligence: countrywide survey in Canada. JMIR Med Educ. 2022;8(1):1–17. source

      Not checked in the free run.

    • cites [8] Pinto dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640–6 Available from: http://link.springer.com/10.1007/s00330-018-5601-1. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Institutional ethics approval was obtained from Queen's University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board.

    “Institutional ethics approval was obtained by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen’s University (ID# 6031912).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The survey was piloted by medical students on the research team not involved in survey instrument creation to ensure question clarity and accessibility.

    “The survey was piloted by medical students on the research team that were not involved in survey instrument creation to ensure question clarity and accessibility of Microsoft Forms (PG, JDP, VV, WL); no formal pretest was performed.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The interview component was developed in accordance with COREQ guidelines.

    “The interview study component was developed in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines for qualitative research reporting [11].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57 Available from: https://academic.oup.com/intqhc/article-lookup/doi/10.1093/intqhc/mzm042. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    No participants declined to interview or dropped-out from interviews, although three students were invited to interview and did not respond.

    “No participants declined to interview or dropped-out from interviews, although three students were invited to interview and did not respond.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The survey was developed in accordance with CROSS guidelines.

    “A 56 question survey was developed in accordance with the Consensus-Based Checklist for Reporting of Survey Studies (CROSS) guidelines for survey studies and coded in Microsoft Forms (Additional file 2 [9];).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Sharma A, Minh Duc NT, Luu Lam Thang T, Nam NH, Ng SJ, Abbas KS, et al. A consensus-based checklist for reporting of survey studies (CROSS). J Gen Intern Med. 2021;36(10):3179–87. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    Two of the interview participants were personally known to AP and RR, while all participants were unfamiliar to the second interviewer and the rest of the research design team.

    “Two of the interview participants were personally known to AP and RR, while all participants were unfamiliar to the second interviewer and the rest of the research design team (NC, FYM).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Email communication and regular meetings between team members supported accurate, unbiased coding, ameliorating personal biases or preconceptions.

    “No coding diary was maintained during the analysis process, with no formal comment on bias or emerging themes; instead email communication and regular meetings between team members supported accurate, unbiased coding, ameliorating personal biases or preconceptions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    Participants who failed the screening questions or did not provide responses to any of the questions were removed from the data.

    “Participants who failed the screening questions or did not provide responses to any of the questions were removed from the data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    All survey responses were anonymous to the research team, although email addresses were collected to contact participants for interviews and prevent multiple responses.

    “All responses were anonymous to the research team, although emails addresses were collected to contact participants for interviews and to prevent multiple responses from participants.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    A stratified random sampling method was employed to interview participants from each English-speaking medical school, using a random number generator to select two participants from each school.

    “In an effort to interview participants from each of the English speaking medical schools (14 of the 17 medical schools), a stratified random sampling method was employed. A random number generator was used to select two participants from each school, who were then invited to interview.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    Participant bias likely affected study outcomes, with respondents more likely to possess interest or knowledge of AI and stronger technical understanding than non-respondents.

    “It is likely that participant bias affected study outcomes, and that respondents were more likely to possess interest or knowledge of AI and have a stronger technical understanding than non-respondents.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Some aspects of the study design had potential to introduce bias or error.

    “Finally, some aspects of the study design had potential to introduce bias or error.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    The study's findings did not support anxiety related to physician replacement among medical students and were more congruent with a European survey by Pinto dos Santos et al. (2021).

    “Our findings are more congruent with a European survey performed by Pinto dos Santos et al. (2021), and did not support anxiety related to physician replacement among medical students.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Pinto dos Santos et al. (2021)

      Not checked in the free run.

  • May combine two claims.

    Study design and conductnone given

    No formal validation process for the survey was employed outside of an internal pilot to ensure question clarity, which could have reduced face or construct validity.

    “No formal validation process for the survey was employed, outside of the internal pilot to ensure question clarity; this could have reduced face validity or construct validity.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey instrument's internal pilot was sufficient to ensure question clarity and validity despite no formal validation.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    The survey instrument was lengthy and included questions beyond the scope of the research question; its length could have contributed to participant non-response.

    “Additionally, our survey instrument was lengthy and included questions that were beyond the scope of our research question. The length could have also contributed to participant non-response.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Medical students in later stages of training (e.g., 3rd and 4th year) and male respondents were underrepresented in the survey results.

    “Another limitation is that medical students in later stages of training (e.g. 3rd and 4th year) and male respondents were underrepresented in the survey results.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The study had non-response and participant bias.

    “The study limitations include non-response and participant bias.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The study was unable to comment on how many participants were recruited through each medium, making study reproduction more challenging.

    “We were also unable to comment on how many participants were recruited through each medium, making study reproduction more challenging.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation

    Teaching future physicians about how AI will integrate into clinical workflow will improve thoughtful implementation of these tools in medical practise and subsequently improve patient care.

    “As AI tools are likely to become widely used in the future, teaching the future generation of physicians about how AI will integrate into clinical workflow will set them up for success, improving the thoughtful implementation of these tools in medical practise and subsequently improving patient care [24].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [24] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56 Available from: https://www.ncbi.nlm.nih.gov/pubmed/30617339. source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductcitation

    The respondent population made up only about 4.5% of the total undergraduate medical student population in Canada, despite responses from all medical schools.

    “While we did receive responses from all medical schools in Canada, our respondent population makes up only ~ 4.5% of the total undergraduate medical student population in Canada [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] The Association of Faculties of Medicine of Canada. Canadian Medical Education Statistics 2019. 2019. Available from: https://www.afmc.ca/sites/default/files/pdf/CMES/CMES2019-Complete_EN.pdf. [Cited 2022 Feb 17].

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The study found a degree of cognitive dissonance among participants: they believed AI would revolutionize medicine while simultaneously believing AI would not directly affect them or their future practise.

    “We also found a degree of cognitive dissonance among participants, as they believed that AI would revolutionize medicine while simultaneously believing AI would not directly affect them or their future practise.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    AI tools are likely to become widely used in the future.

    “As AI tools are likely to become widely used in the future, teaching the future generation of physicians about how AI will integrate into clinical workflow will set them up for success, improving the thoughtful implementation of these tools in medical practise and subsequently improving patient care [24].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [24] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56 Available from: https://www.ncbi.nlm.nih.gov/pubmed/30617339. source

      Not checked in the free run.

48 of 48

  • The paper relies on this without support.

    The AI content that students would be receptive to learning about and the desired mediums of curriculum delivery have not been identified.

    “Additionally, the AI content that students would be receptive to learning about and the desired mediums of curriculum delivery have not been identified.”

  • The paper relies on this without support.

    Curriculum is variable between institutions.

    “Given how variable curriculum can be between institutions, interviews are particularly valuable in exploring institutionally specific opportunities or deficits that cannot be captured by the survey instrument.”

  • The paper relies on this without support.

    Institutionally specific opportunities or deficits about AI education cannot be captured by the survey instrument.

    “Given how variable curriculum can be between institutions, interviews are particularly valuable in exploring institutionally specific opportunities or deficits that cannot be captured by the survey instrument.”

  • The paper relies on this without support.

    The absence of formal AI educational opportunities reported by participants reflects the absence of such opportunities at their medical schools.

    “All participants discussed formal institutional learning opportunities about AI, with all participants but one indicating that there were no formal educational opportunities about AI available to them.”

  • The paper relies on this without support.

    The survey and interview samples are sufficiently representative of Canadian undergraduate medical students for the study's conclusions about lack of opportunities and attitudes.

    “While we did receive responses from all medical schools in Canada, our respondent population makes up only ~ 4.5% of the total undergraduate medical student population in Canada [10].”

  • The paper relies on this without support.

    Self-reported lack of prior AI training accurately reflects actual training history.

    “but indicated they had not received training in formal curriculum previously (85% disagree; 181 strongly disagree, 196 disagree)”

  • The paper relies on this without support.

    The survey instrument's internal pilot was sufficient to ensure question clarity and validity despite no formal validation.

    “No formal validation process for the survey was employed, outside of the internal pilot to ensure question clarity; this could have reduced face validity or construct validity.”

  • The paper relies on this without support.

    The qualitative themes are transferable to the broader Canadian medical student population.

    “Three major themes emerged in the qualitative analysis: a lack of existing learning opportunities about AI, the need to incorporate AI learning in medical curriculum, and positive sentiment about the future of AI in medicine.”
8 of 8

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Survey length is reported as both 32 and 56 questions

    Minor

    The abstract describes a 32-question survey, while the methods describe a 56-question survey.

  • Interview count differs between sections

    Minor

    The abstract reports interviews with 18 students, but the methods report recruiting 17 undergraduate medical students.

2 of 2

Cites

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2 of the 24 references couldn’t be matched to a known paper.

Cited by

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Related

Authors
Pucchio, A., Rathagirishnan, R., Caton, N., Gariscsak, P. J., Del Papa, J., Nabhen, J. J., Vo, V., Lee, W., & Moraes, F. Y.
Affiliations
  • Queen's University (CA)
  • University of British Columbia (CA)
  • Universidade Federal do Paraná (BR)
  • Western University (CA)
  • McMaster University (CA)
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2022-11-28
Volume
22
Issue
1
Pages
815-815
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-022-03896-5
PMC
PMC9703803
OpenAlex
W4310153968
Semantic Scholar
f403d92d262d6b0d099fd9a5177b12871203c597
Cited by
138
References
24
Citations by year
2026: 39 · 2025: 46 · 2024: 38 · 2023: 14 · 2022: 1
FWCI
7.46
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • cIRcle (University of British Columbia) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
artificial intelligence in healthcare, undergraduate medical education, medical students' perceptions, AI curriculum, medical curriculum, AI literacy, Canadian medical schools
MeSH terms
Artificial Intelligence, Canada, Cross-Sectional Studies, Education, Medical, Undergraduate, Humans, Students, Medical

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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