AI in medical education: medical student perception, curriculum recommendations and design suggestions
Open accessAbstract
Medical AI has transformed modern medicine and created a new environment for future doctors. However, medical education has failed to keep pace with these advances, and it is essential to provide systematic education on medical AI to current medical undergraduate and postgraduate students. To address this issue, our study utilized the Unified Theory of Acceptance and Use of Technology model to identify key factors that influence the acceptance and intention to use medical AI. We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI. Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students. The intention to use medical AI is positively associated with factors such as performance expectancy, habit, hedonic motivation, and trust. Therefore, future medical education should prioritize promoting students’ performance in training, and courses should be designed to be both easy to learn and engaging, ensuring that students are equipped with the necessary skills to succeed in their future medical careers.
AI snapshot
Written from the paper’s full text.
TL;DR
This study applied the UTAUT2 model to survey Chinese medical undergraduates and postgraduates about acceptance and intention to use medical AI. Performance expectancy, hedonic motivation, habit, and trust positively predicted intention to use medical AI, while several other UTAUT2 factors were not significant.
Key findings
- Among 1,243 respondents from 13 universities and 33 hospitals, 54.3% reported prior experience using medical AI; postgraduates reported more experience (75.0%) than undergraduates (59.5%, χ2 = 24.4, p < 0.001).
- Significant positive predictors of behavioral intention were performance expectancy (β = 0.146, p < 0.001), hedonic motivation (β = 0.237, p < 0.001), habit (β = 0.388, p < 0.001), and trust (β = 0.276, p < 0.001); behavioral intention also predicted user behavior (β = 0.927, p < 0.001).
- Effort expectancy (β = -0.068, p = 0.101), social influence (β = 0.041, p = 0.346), price value (β = -0.089, p = 0.068), facilitating conditions (β = 0.007, p = 0.895), and technology fear (β = -0.012, p = 0.585) were not significant.
- Measurement model statistics were acceptable: Cronbach’s α 0.833–0.916, composite reliability 0.895–0.947, AVE 0.623–0.856, and all HTMT values below 0.9.
Methods
- Cross-sectional online survey based on UTAUT2, including 38 five-point Likert items across 10 factors; participants were fourth- and fifth-year medical undergraduates and all-grade medical postgraduates, recruited via Wen Juan Xing, WeChat, and the web from 13 universities and 33 hospitals.
- Data analysis used partial least squares in SmartPLS 4.0 for path coefficients; Cronbach’s α, composite reliability, AVE, and HTMT for reliability/validity; chi-square tests in SPSS 26.0 for demographic comparisons.
- The results report that 675 respondents with experience of medical AI completed the survey and were analyzed.
Limitations
- Stated: the online voluntary survey may have attracted students already interested in the topic; data came from 33 hospitals and 11 universities in seven provinces, so generalization to all of China should be cautious; because systematic medical AI education was lacking, participants’ attitudes were shaped by the specific medical AI tools they encountered.
- Evident: the paper reports 13 universities in the abstract/methods but 11 universities in the limitations, an inconsistency in the text.
- Evident: the cross-sectional design and self-reported measures cannot establish causality or objectively verify AI use.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Claims the paper makes
0 of 7 cited sources checked · 0 supported · 0 flagged
- Findingown data
Facilitating conditions had no significant effect on behavioral intention to use medical AI (path coefficient 0.007, p = 0.895).
“H7. Facilitating Conditions → Behavioral Intention | 0.007 (ns) | 0.895”
Quote in the paperNot a substantial claim - Findingown data
Hedonic motivation had a positive influence on behavioral intention to use medical AI (path coefficient 0.237, p < 0.001).
“H4. Hedonic Motivation → Behavioral Intention | 0.237 | 0.000***”
- Also stated in: § Results · plain
“which showed that performance expectancy, hedonic motivation, habit, and trust had a positive influence on the intention to use medical AI.”
Quote in the paperSubstantial claim - Also stated in: § Results · plain
- Findingown data
There was no significant difference between sex and self-evaluation of experience of using AI and medical AI.
“There was no significant difference between sex and self-evaluation of their experience of using AI and medical AI (χ2 = 0.02, p = 0.886).”
Quote in the paperNot a substantial claim - Findingown data
Habit had a positive influence on behavioral intention to use medical AI (path coefficient 0.388, p < 0.001).
“H6. Habit → Behavioral Intention | 0.388 | 0.000***”
- Also stated in: § Results · plain
“which showed that performance expectancy, hedonic motivation, habit, and trust had a positive influence on the intention to use medical AI.”
Quote in the paperSubstantial claim - Also stated in: § Results · plain
- Findingown data
Behavioral intention had a positive effect on usage behavior (path coefficient 0.927, p < 0.001).
“H10. Behavioral Intention → User Behavior | 0.927 | 0.000***”
Quote in the paperSubstantial claim
- Findingown data
Performance expectancy had a positive influence on behavioral intention to use medical AI (path coefficient 0.146, p < 0.001).
“H1. Performance Expectancy → Behavioral Intention | 0.146 | 0.000***”
- Also stated in: § Results · plain
“which showed that performance expectancy, hedonic motivation, habit, and trust had a positive influence on the intention to use medical AI.”
Quote in the paperSubstantial claim - Also stated in: § Results · plain
- Findingown data
Technology fear had no significant effect on behavioral intention to use medical AI (path coefficient -0.012, p = 0.585).
“H8. Technology Fear → Behavioral Intention | -0.012 (ns) | 0.585”
Quote in the paperNot a substantial claim - Findingown data
A significantly higher proportion of postgraduate students (75.0%) reported experience of using medical AI compared to undergraduate students (59.5%).
“However, a significantly higher proportion of postgraduates (75.0%) reported had experience of using medical AI compared to undergraduates (59.5%, χ2 = 24.4, p < 0.001).”
- Also stated in: § Abstract · hedged
“Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students.”
Quote in the paperNot a substantial claim - Also stated in: § Abstract · hedged
- Findingown data
1,243 undergraduate and postgraduate medical students from 13 universities and 33 hospitals completed the questionnaire.
“During a 2-week period, 1,243 undergraduate and postgraduate medical students from 13 universities and 33 hospitals completed the questionnaire.”
- Also stated in: § Abstract · plain
“We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI.”
Quote in the paperSubstantial claim - Also stated in: § Abstract · plain
- Findingown data
Price value had no significant effect on behavioral intention to use medical AI (path coefficient -0.089, p = 0.068).
“H5. Price Value → Behavioral Intention | -0.089 (ns) | 0.068”
Quote in the paperNot a substantial claim - Findingown data
Effort expectancy had no significant effect on behavioral intention to use medical AI (path coefficient -0.068, p = 0.101).
“H2. Effort Expectancy → Behavioral Intention | -0.068 (ns) | 0.101”
Quote in the paperNot a substantial claim - Findingown data
54.3% (675) of the surveyed medical students reported prior experience using medical AI.
“and 54.3% reported prior experience using medical AI”
- Also stated in: § Results · plain
“Finally, a total of 675 respondents with experience of medical AI completed the survey.”
- Also stated in: § For the future medical AI education and design · hedged
“Our findings revealed that only 675 (54.3%) participants self-reported familiarity with medical AI, indicating that nearly half of the medical students were not acquainted with this field.”
Quote in the paperSubstantial claim - Also stated in: § Results · plain
- Findingown data
Trust had a positive influence on behavioral intention to use medical AI (path coefficient 0.276, p < 0.001).
“H9. Technology Trust → Behavioral Intention | 0.276 | 0.000***”
- Also stated in: § Results · plain
“which showed that performance expectancy, hedonic motivation, habit, and trust had a positive influence on the intention to use medical AI.”
Quote in the paperSubstantial claim - Also stated in: § Results · plain
- Findingown data
Social influence had no significant effect on behavioral intention to use medical AI (path coefficient 0.041, p = 0.346).
“H3. Social Influence → Behavioral Intention | 0.041 (ns) | 0.346”
Quote in the paperNot a substantial claim - Premisecitation
Medical education has not kept pace with the remarkable breakthroughs made in AI.
“Despite increasing interest in this technology, medical education has not kept pace with the remarkable breakthroughs made in AI [12].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] Han ER, Yeo S, Kim MJ, Lee YH, Park KH, 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. source
Not checked in the free run.
- Study design and conductown data
Discriminant validity of the measurement model was established, with all Heterotrait-Monotrait ratio values below 0.9.
“To assess the discriminant validity of the measurement model, we used the restrictive method of the HTMT, which ensured that all values were below 0.9.”
Quote in the paperNot a substantial claim - Premisecitation
Medical students are generally enthusiastic about learning and using AI in their practice.
“However, they are generally enthusiastic about learning and using AI in their practice [16, 17].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [16] Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Poon DS. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11(1):14. source
Not checked in the free run.
- cites [17] Blease C, Kharko A, Bernstein M, Bradley C, Houston M, Walsh I, Hagglund M, DesRoches C, Mandl KD. Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland. BMJ Health Care Inform 2022, 29(1). source
Not checked in the free run.
- Premisereasoning
Because the UTAUT2 model has demonstrated generalizability across domains, the determinants of acceptance it identifies may serve as promising predictors for acceptance of medical AI.
“As the UTAUT2 model has demonstrated generalizability, the determinants of acceptance identified by the model may also serve as promising predictors for acceptance of medical AI.”
Quote in the paperSubstantial claim
- May combine two claims. 4
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 2
May combine two claims. The quote only partly states this claim.
Study design and conductown dataThe measurement model showed acceptable internal consistency and convergent validity, with Cronbach's α ranging from 0.833 to 0.916, composite reliability from 0.895 to 0.947, and AVE from 0.623 to 0.856.
“Table 1 showed the calculated values of Cronbach’s α, which ranged from 0.833 to 0.916, while CR varied in the range of 0.895–0.947 and AVE ranged from 0.623 to 0.856.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe results should be generalized to the whole of China with caution because the data came from 33 hospitals and 11 universities in seven provinces.
“Secondly, while our data was collected from 33 hospitals and 11 universities in seven provinces of China, caution should be exercised when generalizing the results to the entire country.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe participants' perspectives are shaped by their interactions with the specific medical AI tools they use in routine practice, producing a spectrum of attitudes toward different medical AI applications.
“Consequently, the perspectives conveyed by the participants are inevitably shaped by their interactions with the specific medical AI tools they engage with in their routine practices. This variation in exposure has inherently led to a spectrum of attitudes towards different medical AI applications.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe online survey method and voluntary participation may have introduced a bias by attracting only students interested in the subject matter.
“Firstly, the online survey method and voluntary participation may have introduced a bias by only attracting students interested in the subject matter.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe intention to use a technology directly predicts actual usage.
“Its core principle is that the intention to use technology directly predicts actual usage [19].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] Cabrera-Sánchez J-P, Villarejo-Ramos ÁF, Liébana-Cabanillas F, Shaikh AA. Identifying relevant segments of AI applications adopters – expanding the UTAUT2’s variables. Telematics Inform. 2021;58:101529. source
Not checked in the free run.
May combine two claims.
PremisecitationMedical students are often not familiar with AI and may be worried about the potential for job loss.
“Previous studies have shown that medical students are often not familiar with AI and may be worried about the potential for job loss [13–15].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [13] Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baessler B. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640–6. source
Not checked in the free run.
- cites [14] Bisdas S, Topriceanu CC, Zakrzewska Z, Irimia AV, Shakallis L, Subhash J, Casapu MM, Leon-Rojas J, Pinto Dos Santos D, Andrews DM, et al. Artificial Intelligence in Medicine: a multinational Multi-Center Survey on the Medical and Dental Students’ perception. Front Public Health. 2021;9:795284. source
Not checked in the free run.
- cites [15] Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician confidence in Artificial Intelligence: an Online Mobile Survey. J Med Internet Res. 2019;21(3):e12422. source
Not checked in the free run.
The paper relies on this without support.
There is currently a lack of research exploring the significance of medical AI within the context of medical education in China.
“Moreover, there is currently a lack of research exploring the significance of medical AI within the context of medical education in China.”
The paper relies on this without support.
There has been limited research on the acceptance and intention to use medical AI among medical undergraduate and postgraduate students.
“However, there has been limited research on the acceptance and intention to use medical AI among medical undergraduate and postgraduate students.”
The paper relies on this without support.
A limited number of medical schools in China had incorporated medical AI into their curriculum for students.
“Furthermore, a limited number of medical schools in China had incorporated medical AI into their curriculum for students.”
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?
Contributions elaborated mainly or solely by medical students strongly call for the inclusion of AI education in medical curricula; students were enthusiastic about learning AI and recognized its crucial role in the future of medicine, but reported a lack of formal AI training and insufficient AI-related learning opportunities; researchers and educators also demanded inclusion of AI in medical Cur
“Finally, contributions elaborated mainly or solely by medical students strongly call for the inclusion of AI education in medical curricula [50–54]. Students were enthusiastic about learning AI and recognized its crucial role in the future of medicine [45, 55]. However, they reported a lack of formal AI training and insufficient AI-related learning opportunities in their medical education [46, 56,”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [55]
Split into 3 atoms — 0 confirmed, 3 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
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.
University count changes from 13 to 11
MinorThe results say the survey included students from 13 universities, but the limitations section says the data came from 11 universities.
“During a 2-week period, 1,243 undergraduate and postgraduate medical students from 13 universities and 33 hospitals completed the questionnaire.”
“our data was collected from 33 hospitals and 11 universities in seven provinces of China”
H10 concerns distance education rather than medical AI
MinorThe paper says all 10 hypotheses concern medical AI, but H10 instead specifies usage behavior in distance education systems.
“Behavioral intention has a positive effect on usage behavior in distance education systems.”
“we formulated 10 hypotheses to test key factors that exerted a significant influence on the acceptance and intention to use of medical AI among both medical undergraduate and postgraduate students.”
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- Authors
- Li, Q., & Qin, Y.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Qin, Yunhao
- ORCID
- Li, Qianying
- Venue
- BMC Medical Education
- Published
- 2023-11-09
- Volume
- 23
- Issue
- 1
- Pages
- 852-852
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC10637014
- OpenAlex
- W4388522403
- Cited by
- 131
- References
- 30
- Citations by year
- 2026: 53 · 2025: 56 · 2024: 21 · 2023: 1
- FWCI
- 5.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (8180090624)
- 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, medical students, technology acceptance, UTAUT, intention to use, trust in AI, performance expectancy, hedonic motivation, habit, undergraduate vs graduate students, curriculum development, AI awareness
- MeSH terms
- Curriculum, Education, Medical, Education, Medical, Undergraduate, Humans, Perception, Students, Medical
About these checks
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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