Currently submitted to: JMIR Formative Research
Date Submitted: May 24, 2026
Open Peer Review Period: Jun 19, 2026 - Aug 14, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Application, Educational Value, and Ethical Concerns of DeepSeek in Healthcare: A Nationwide Cross-Sectional Survey Among Medical Professionals in China
ABSTRACT
Background:
Large language models (LLMs) are increasingly explored for clinical documentation and medical education. However, real-world evidence regarding clinician adoption and perceptions of open-source LLMs in China remains limited.
Objective:
This exploratory nationwide survey aimed to characterize institutional deployment patterns, clinician-reported use, and perceived barriers related to DeepSeek-assisted clinical documentation.
Methods:
We conducted a multicenter cross-sectional online survey among medical professionals from 102 medical institutions across 31 province-level regions in China. Participants were recruited through professional academic networks and clinician-oriented online platforms using convenience-based dissemination strategies. Survey items assessed institutional deployment, prior LLM experience, DeepSeek-assisted documentation use, educational perceptions, and ethical concerns.
Results:
A total of 571 medical professionals from 102 medical institutions across 31 province-level regions in China completed the survey. Institutional deployment of DeepSeek was reported by 43.3% of respondents and was significantly higher in eastern China than in central and western regions (47.6% vs 29.3% vs 23.1%, P<0.001). Nearly half of participants (47.8%) reported prior LLM experience, which differed according to gender (male: 55.2% vs female: 41.3%, P=0.001), educational level (P<0.001), and clinical role (P=0.015). Despite institutional adoption and prior LLM experience, 80.4% (450/571) of respondents had never used DeepSeek-assisted clinical documentation. Among active users, DeepSeek was mainly applied to progress notes (52.7%, 59/112) and medical history documentation (50.9%, 57/112). Ethical concerns were reported by 52.5% (300/571) of respondents, while most participants also endorsed the clinical and educational value of DeepSeek, particularly for residency training (74.0%, 422/571) and professional development (74.4%, 425/571).
Conclusions:
This exploratory nationwide survey suggests that early adoption of DeepSeek-assisted clinical documentation in China remains uneven across regions and healthcare settings. Although clinicians reported perceived benefits for workflow efficiency and residency education, substantial technical and ethical concerns continue to limit broader implementation. Further implementation-focused and longitudinal studies are needed to support sustainable integration of LLM technologies into clinical practice and medical training. Clinical Trial: none
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