Large Language Model Assistance for Clinical Decision-Making Among Rural Physicians

Rural physicians play an essential role in the diagnosis and management of common and frequently occurring conditions, referral decision-making, chronic disease management, and patient education. In resource-constrained primary care settings, they often face limited access to medical information and specialist support, delays in updating clinical knowledge and guidelines, and substantial pressure in clinical decision-making. These challenges are particularly relevant in northwestern China, where primary care resources are relatively limited. Improving rural physicians' abilities in diagnostic assessment, recognition of clinical warning signs, and rational prescribing is therefore an important priority for strengthening primary healthcare services.

Large language models (LLMs) can support medical information retrieval, organization of diagnostic and management approaches, differential diagnosis, medication-related decision-making, patient education, and follow-up planning, and may therefore serve as accessible tools for supporting clinical decision-making in primary care. However, general-purpose LLMs were not specifically developed for use in resource-constrained primary care settings and have not been adequately evaluated among rural physicians. Their responses may contain factual errors or fabricated evidence, overlook warning signs, provide insufficient medication safety warnings, or recommend investigations and treatments that are not feasible in local primary care settings. Without adequate verification skills, physicians may fail to benefit from LLM assistance and may even introduce new safety risks. It is therefore important to evaluate how rural physicians use LLMs and whether structured training can improve the safe and effective use of these tools before their wider implementation.

This randomized controlled trial will evaluate the effects of LLM assistance and brief training on clinical decision-making among rural physicians. Participants will complete clinical cases involving common conditions encountered in primary care, with tasks assessing diagnostic judgment, recognition of warning signs, rational treatment, and patient education. Some participants will also use the LLM as a second-opinion tool to review and revise their initial decisions. All responses will be independently evaluated by reviewers blinded to group assignment using standardized scoring criteria to assess overall clinical decision-making performance and safety.

Inclusion criteria

• Currently engaged in clinical practice at a rural primary healthcare institution in northwestern China.
• Has received formal medical education and holds a relevant diploma or degree.
• Able to read and understand clinical case materials in Chinese.
• Able to use a computer to complete the study tasks.
• Willing to participate and able to provide written informed consent.

Exclusion criteria

• Previously involved in the development of the clinical case tasks, reference answers, or scoring rubric for this study.
• Previously participated in pilot testing involving the same clinical case tasks or study procedures.