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Cited by 63Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationRadiology practices and education

The importance of introducing artificial intelligence to the medical curriculum – assessing practitioners’ perspectives

Open access
Dumić-Čule, Ivo, Orešković, Tin, Brkljačić, Boris, Kujundžić Tiljak, Mirjana, Orešković, Stjepan·2020·Croatian Medical Journal
DOI 10.3325/cmj.2020.61.457PMC PMC7684542
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Abstract

AIM: To assess the attitude about the importance of introducing education on artificial intelligence (AI) in medical schools' curricula among physicians whose everyday job is significantly impacted by AI. METHODS: An anonymous questionnaire was distributed at the national level in Croatia among radiologists and radiology residents practicing in primary, secondary, and tertiary health care institutions, both in the private and the public sectors. The overall response rate was 45% (144 of 321). RESULTS: A large majority of participants - 89.6% (95% Agresti-Coull confidence interval 0.83-0.94) agreed on the need for education on AI to be included in medical curricula. Answers revealed a very high support across age groups and regardless of subspecialty area. A slightly higher support was present among physicians working in university hospitals compared with those in primary care centers, and among radiology residents compared with radiologists - but these estimated differences are uncertain, and the support levels were clearly high across the considered variables. CONCLUSION: Since medical students have previously been shown to support introducing education on AI, a growing literature argues the same for reasons here reviewed, and physicians practicing a highly relevant area (radiology) overwhelmingly agree, we conclude that medical schools should indeed take steps to keep pace with technological progress in medicine by including education on AI in their curricula, be it as part of existing or new courses.

AI snapshot

Written from the paper’s full text.

TL;DR

A national Croatian survey of radiologists and radiology residents found overwhelming support for including artificial intelligence (AI) education in medical school curricula. The authors conclude that medical schools should incorporate AI education into existing or new courses.

Key findings

  • Among 144 respondents, 89.6% agreed that education on AI should be part of medical schools’ curricula (95% Agresti-Coull CI 0.83-0.94).
  • Support was very high across age groups and subspecialty areas, and logistic regression yielded no evidence that attitude varied widely by training, gender, or institution type.
  • Radiology residents showed up to a ~16% higher estimated probability of answering affirmatively than radiologists, but the association was indistinguishable from 0 when generalizing.
  • The authors note this support is substantially higher than the 71% reported in an earlier medical student survey they discuss.

Methods

  • An anonymous electronic questionnaire was distributed at the national level in Croatia via email to 321 members of the Croatian Radiology Society, including radiologists and radiology residents in primary, secondary, and tertiary care, private and public sectors.
  • The overall response rate was 45% (144 of 321); respondents were 62.5% radiologists and 37.5% radiology residents, and 54.86% were female.
  • Main analysis estimated the sample proportion of positive answers with a 95% Agresti-Coull confidence interval; logistic regression included training, gender, and institution type, while age group and subspecialty were explored graphically.

Limitations

  • The paper states that voluntary surveys carry potential non-response bias, especially if more enthusiastic individuals were more likely to participate, and that the online format may have been more comfortable for those inclined toward technology.
  • The authors argue these concerns are mitigated by the relatively high response rate, the number of participants, and the lack of significant sample-population differences in known traits (gender, training), so further statistical adjustment was not necessary.
  • Evident from the text: the study is cross-sectional, self-reported, single-country, and limited to one specialty; the private-practice logistic estimate is highly uncertain (coefficient 16, SE 1602.1), and age group and subspecialty were not included in the logistic regression model.

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

Integrity signals

Registry

Crossref · 10.3325/cmj.2020.61.457

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 16 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Support for including AI education in medical curricula was very high regardless of subspecialty area.

    “Answers revealed a very high support across age groups and regardless of subspecialty area.”
    • Also stated in: § RESULTS · plain
      “respondents held a high level of support for introducing AI education regardless of their area of subspecialty (Figure 3).”
    Quote in the paperSubstantial claim
  • Findingown data

    There was no significant difference between men and women in the probability of thinking AI should be part of the curriculum.

    “Similarly, considering the estimate and the standard error, there was no significant difference in how probable men and women were to think AI should be part of the curriculum.”
    Quote in the paperNot a substantial claim
  • Findingown data

    The logistic regression yielded no evidence that attitude varied widely across the included variables (training, gender, institution type).

    “In short, the logistic regression yielded no evidence that the attitude varied widely across the included variables.”
    Quote in the paperSubstantial claim
  • Findingown data

    Support for including AI education in medical curricula was very high across age groups.

    “Answers revealed a very high support across age groups and regardless of subspecialty area.”
    • Also stated in: § RESULTS · plain
      “we resist reading too much into the patterns and take from this simply that the support is very high across age groups.”
    Quote in the paperSubstantial claim
  • Findingown data

    Among radiologists and radiology residents in Croatia, 89.6% (95% Agresti-Coull confidence interval 0.83-0.94) agreed that education on AI should be part of medical schools' curricula.

    “A large majority of participants – 89.6% (95% Agresti-Coull confidence interval 0.83-0.94) agreed on the need for education on AI to be included in medical curricula.”
    • Also stated in: § RESULTS · plain
      “The percentage of respondents who stated that education on AI should be part of medical schools’ curricula was a very high 89.6% (Figure 1).”
    • Also stated in: § RESULTS · hedged
      “that ~ 89% (CI 0.83-0.94) of radiology practitioners believed education on AI should be part of medical schools’ curricula.”
    Quote in the paperSubstantial claim
  • May combine two claims. 12
  • Classified as probably not a substantial claim. 6

  • May combine two claims.

    Findingown data

    There was slightly higher support among radiology residents compared with radiologists, but the estimated difference was uncertain.

    “A slightly higher support was present among physicians working in university hospitals compared with those in primary care centers, and among radiology residents compared with radiologists – but these estimated differences are uncertain, and the support levels were clearly high across the considered variables.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    There was slightly higher support among physicians working in university hospitals compared with those in primary care centers, but the estimated difference was uncertain.

    “A slightly higher support was present among physicians working in university hospitals compared with those in primary care centers, and among radiology residents compared with radiologists – but these estimated differences are uncertain, and the support levels were clearly high across the considered variables.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Being a radiology resident was associated with up to a ~16% increase in the estimated probability of answering affirmatively compared with radiologists, but the standard error made the association indistinguishable from 0 when generalizing.

    “Compared with radiologists – assuming the other variables hold the same value for radiologists – being a radiology resident was associated with up to a ~ 16% increase in the estimated probability of answering affirmatively, but the standard error makes the association indistinguishable from 0 when generalizing (Table 1).”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: When interpreting the association between being a radiology resident and answering affirmatively, the other variables are assumed to hold the same value for radiologists.

  • May combine two claims.

    Findingown data

    Working at any of the three remaining institution types (general hospital, university hospital, private practice) was positively associated with answering affirmatively compared with working at a primary care facility, but uncertainty prevented generalization beyond the sample.

    “Further, compared with working at a primary care facility, working at any of the three remaining institution types was positively associated with answering affirmatively; however, the uncertainty of the estimates again prevents generalization beyond the sample.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The results are generalizable to other similarly impacted areas (dermatology, ophthalmology, pathology) and serve as a look into future attitudes among practitioners of other fields yet to be significantly influenced by AI.

    “Since radiology is one of the several areas thus far most impacted by AI innovation, our results are arguably both (i) generalizable to the other similarly impacted areas (dermatology, ophthalmology, pathology), and (ii) serve as a look into the future attitudes to be expected among practitioners of other fields yet to be significantly influenced by AI.”
    Quote in the paperNot a substantial claim

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

    Study design and conductown data

    The overall response rate was 45% (144 of 321).

    “The overall response rate was 45% (144 of 321).”
    • Also stated in: § RESULTS · plain
      “The overall response rate was 45% (144 of 321).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The support rate among radiology practitioners (89.6%) is substantially higher than the rate reported among medical students (71%), suggesting practitioners recognize the need more clearly than students.

    “That a majority of respondents would support an introduction of education on AI is not surprising, but this rate is substantially higher even than the rates reported in the above-discussed studies: it suggests that practitioners recognize the need even more clearly than students (in an above-discussed student survey, 71% percent supported introducing AI education).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [12] Pinto Dos Santos D Giese D Brodehl S Chon SH Staab W Kleinert R Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol 2019 29 1640 6 10.1007/s00330-018-5601-1 29980928

      Not checked in the free run.

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

    Study design and conductreasoning

    The introductory message contained no background information on the subject matter nor references to suggested reading material, to avoid biasing responses.

    “The introductory message contained no background information on the subject matter, nor any references to suggested reading material, to avoid biasing the responses.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    In a UK survey of medical students, 88.8% anticipated AI training would be beneficial for their future careers and believed it should be part of mandatory curricula.

    “A majority (88.8%) of the students anticipated AI training would be beneficial for their future careers and believed it should be a part of mandatory curricula.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] Sit C Srinivasan R Amlani A Muthuswamy K Azam A Monzon L Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging 2020 11 14 10.1186/s13244-019-0830-7 32025951 PMC7002761

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Education on AI is an important factor in the development of attitudes toward AI and is offered by major radiological associations as part of continuing medical education.

    “An important factor in the development of these attitudes is education on AI, which is nowadays offered by major radiological associations as part of continuing medical education (11).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [11] RSNA. [2019-09-01]. RSNA Spotlight Course Available from: https://www.rsna.org/spotlight/ai-san-francisco. Accessed: October 22, 2020.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    In the same UK survey, 89.6% of medical students said they would not feel capable to work with AI upon graduation given their knowledge at the time.

    “Most respondents emphasized (89.6%), however, that with the state of their knowledge at the time of answering the question they would not feel capable to work with AI upon graduation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [13] Sit C Srinivasan R Amlani A Muthuswamy K Azam A Monzon L Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging 2020 11 14 10.1186/s13244-019-0830-7 32025951 PMC7002761

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Recent research suggests most radiologists have a favorable attitude toward AI, strive to be included in AI education and research, and would be willing to help develop AI tools.

    “recent research suggests that most radiologists strive to be included in AI education and research; that, on average, they would be willing to help in developing AI tools; and that they generally have a favorable attitude toward AI (10).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [10] European Society of Radiology (ESR) Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019 10 105 10.1186/s13244-019-0798-3 31673823 PMC6823335

      Not checked in the free run.

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

    Premisecitation

    Learning outcomes and competencies attained by completion of medical school are globally similar, with main focus on exposure to an immense amount of medical information and learning how to apply them in patient care.

    “As described by the Accreditation Council for Graduating Medical Education, learning outcomes and competencies attained by completion of medical school are nowadays globally similar, with the main focus on exposure to an immense amount of medical information and learning how to apply them in patient care (23).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [23] Philibert I Nasca TJ The next accreditation system: stakeholder expectations and dialogue with the community. J Grad Med Educ 2012 4 276 8 10.4300/JGME-04-02-35 23730461 PMC3399632

      Not checked in the free run.

  • May combine two claims.

    Premisenone given

    Systematic change in mandatory medical curricula to include AI has still not occurred despite successful initiatives at certain institutions.

    “Notwithstanding the successful initiatives at certain institutions, systematic change in mandatory medical curricula has still not occurred.”
    Quote in the paperSubstantial claim
14 of 14

  • The paper relies on this without support.

    The population parameters for gender and training distribution of radiologists and radiology residents in Croatia are accurate.

    “which are the population parameters known in Croatia.”

  • The authors state this as an assumption.

    When interpreting the association between being a radiology resident and answering affirmatively, the other variables are assumed to hold the same value for radiologists.

    “Compared with radiologists – assuming the other variables hold the same value for radiologists – being a radiology resident was associated with up to a ~ 16% increase in the estimated probability of answering affirmatively, but the standard error makes the association indistinguishable from 0 when generalizing (Table 1).”
2 of 2

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?

  • Most (>30) studies used other instruments, which were developed de novo for the respective studies.

    “Most of them, however, (>30) used other instruments, which were developed de novo for the respective studies [37]”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [37]

    The Materials and Methods section (page unavailable) describes this paper’s own electronic survey, not a comparison of instruments used across studies. Searches for de novo instruments and questionnaire development found no passage supporting the claim that more than 30 studies used instruments developed for their respective studies. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • As medicine and the delivery of healthcare enters the age of AI, the need for competent human-machine interaction for the use of data to aid clinical decision making will rise.

    “As medicine and the delivery of healthcare enters the age of AI, the need for competent human–machine interaction for the use of data to aid clinical decision making will rise. 23”

    From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [23]

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [24]American Medical Association. 2016. [2019-08-01]. Digital Health Study Physicians’ motivations and requirements for adopting digital clinical tools. Available from: https://www.ama-assn.org/practice-management/digital. Accessed: October 22, 2020.

Cited by

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Related

Authors
Dumić-Čule, I., Orešković, T., Brkljačić, B., Kujundžić Tiljak, M., & Orešković, S.
Affiliations
  • Children's Hospital Srebrnjak (HR)
  • IBM (United States) (US)
  • University of Zagreb (HR)
Corresponding author
Dumić-Čule, Ivo
ORCID
Venue
Croatian Medical Journal
Publisher
Croatian Medical Journals
Published
2020-10
Volume
61
Issue
5
Pages
457-464
ISSN
  • 0353-9504 (ISSN-L)
  • 1332-8166
DOI
10.3325/cmj.2020.61.457
PMC
PMC7684542
OpenAlex
W3094858067
Semantic Scholar
3f9d4a480bb17c6a272e3fce53252ad4dea6987c
Cited by
61
References
23
Citations by year
2026: 5 · 2025: 12 · 2024: 17 · 2023: 14 · 2022: 8 · 2021: 5
FWCI
1.99
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Croatian Medical Journal (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Zagreb University Computing Centre (SRCE) (submittedVersion, other-oa)
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
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
Keywords
artificial intelligence, medical education, medical curriculum, physician attitudes, radiologists, radiology residents, primary care, university hospital, clinical implementation of AI
MeSH terms
Artificial Intelligence, Attitude of Health Personnel, Career Choice, Curriculum, Female, Humans, Male, Primary Health Care, Surveys and Questionnaires, Radiology, Schools, Medical, Students, Medical, Croatia

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