OBJECTIVES: A paper from Goh et al. found that a large language model (LLM) working alone outperformed American clinicians assisted by the same LLM in diagnostic reasoning tests (Goh E, Gallo R, Hom J, Strong E, Weng J, Kerman H, et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open 2024;7:e2440969). We aimed to replicate this experiment in a UK setting and explore how interactions with the LLM might explain the observed gaps in performance. METHODS: This was a within-subjects study of UK physicians. 22 participants answered structured questions on four clinical vignettes. For 2 cases physicians had access to an LLM via a custom-built web-application. Results were analysed using a mixed-effects model accounting for case difficulty and the variability of clinicians at baseline. Qualitative analysis involved coding of participant-LLM interaction logs and evaluating the rates of LLM use per question. RESULTS: Physicians with LLM assistance scored significantly lower than the LLM alone (mean difference 21.3 percentage points, p<0.001). Access to the LLM was associated with improved physician performance compared to using conventional resources (74.3 vs. 65.7 %, p=0.001). There was significant heterogeneity in the degree of LLM-assisted improvement (SD 12.8 %). Qualitative analysis revealed that only 30 % of case questions were directly posed to the LLM, which suggests that under-utilisation of the LLM contributed to the observed performance gap. CONCLUSIONS: While access to an LLM can improve diagnostic accuracy, realising the full potential of human-AI collaboration may require a focus on training clinicians to integrate these tools into their cognitive workflows and on designing systems that make these integrations the default rather than an optional extra.
Healy et al. (2026) studied this question.