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This study aims to assess the role of large language models in supporting clinical decision-making across various specialties and experience levels.

Augmenting clinical decision-making with large language models: evaluation across general and specialty tasks

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This study aims to assess the role of large language models in supporting clinical decision-making across various specialties and experience levels.
Evaluated 3 LLMs (Deepseek-R1, GPT-4o-mini, LLaMA-4) with general-disease and prostate disease tasks.
Clinicians of differing experience levels completed tasks before and after reviewing LLM responses.
Performance rated on a 5-point Likert scale, analyzed using Wilcoxon signed-rank test.
LLM assistance significantly improved clinician performance in general disease tasks (P < .05).
Junior clinicians' scores increased by 15.9%-20.8% in specialty scenarios, surpassing senior clinicians' unaided performance (P < .05).
Deepseek-R1 outperformed others in diagnostic tasks.

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Randomized trial evaluates LLMs' support for clinicians in decision-making, suggesting improved performance and reduced disparities.

Tao et al. (2026) studied this question.

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