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TRUVACE RECORD VERSION
record: TRV-2026-2357
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-04T00:02:14.043152Z
status: published
lens: p_space
sector: health
headline: Adoption, usage patterns, and perceptions of DeepSeek among medical interns: Evidence from a Chinese Teaching Hospital
dek: Artificial intelligence (AI)-driven chatbots based on large language models are increasingly entering medical education, but evidence on the real-world use of domestic systems such as DeepSeek among Chinese medical interns is still scarce. This cross-sectional study investigated the prevalence, patterns and perceptions of DeepSeek use among medical interns at the Second Affiliated Hospital of Anhui Medical University. A total of 634 interns from clinical medicine, preventive medicine and nursing completed an ano…
gain_title: (none)
problem_title: DeepSeek uptake among interns differed by residence and major, with rural and preventive medicine interns less likely to use it, pointing to uneven access to AI tools in medical training.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: DeepSeek uptake among interns differed by residence and major, with rural and preventive medicine interns less likely to use it, pointing to uneven access to AI tools in medical training.
problem_evidence: Urban interns were more likely to report using DeepSeek than rural interns. | preventive medicine interns were less likely to report use
quick_read: A cross-sectional questionnaire at the Second Affiliated Hospital of Anhui Medical University asked 634 interns in clinical medicine, preventive medicine and nursing about their use of the DeepSeek chatbot. About one-third reported using it, and over half of users did so several times a week, mostly for assignments, information searches, translation and polishing text.

The findings show that AI chatbots are becoming part of everyday medical training, though not evenly across groups. Because the data are self-reported at a single hospital, they cannot show whether use improves learning or how accurate the outputs were, and institutions are still working out guidance on verification and academic integrity.
limitation: The study is a cross-sectional questionnaire at a single hospital that provides only descriptive evidence, so it cannot establish effects on learning or accuracy of the AI output.
tag: Evidence-backed problem
key_points: About one-third of the 634 surveyed interns reported using DeepSeek. | Over half of users used it several times per week. | Common uses were assignments, information search, translation and text polishing; fewer used it for data analysis. | After adjustment, residence and major were associated with use, while age and sex were not. | Most users saw its advantages and disadvantages as balanced, and only a minority thought the disadvantages outweighed the advantages.
rundown: The study surveyed interns from three majors anonymously: clinical medicine, preventive medicine and nursing. Nursing interns were more likely to use DeepSeek than clinical medicine interns, while preventive medicine interns were less likely.

Among users, attitudes were mainly neutral or optimistic and differed across majors. Usage frequency and the perceived balance of advantages and disadvantages did not differ significantly by major.

The authors describe the results as descriptive evidence that may inform future institutional guidance on responsible use, verification of AI-generated information and academic integrity.
sources:
- peer_reviewed | PLOS One | https://doi.org/10.1371/journal.pone.0362171 | 2026-11-02
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