ArticleUpdates in surgery2026
Assessing the performance of three large language models in thyroid cancer tumour board decision-making.
Article in Updates in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Large language models (LLMs) have potential to support clinical decision-making, but their role in thyroid cancer multidisciplinary team (MDT) meetings remains uncertain. This study evaluated the performance of ChatGPT GPT-5.5, Muse Spark 1.0 (Meta AI) and DeepSeek-V4-Flash in reproducing the management decisions of a regional thyroid cancer MDT. One hundred thyroid cancer cases discussed by a regional MDT were submitted to each LLM using an identical standardised prompt referencing ATA, BTA and UICC guidelines. Recommendations were independently assessed by four consultant endocrine surgeons using a four-point concordance scale (0-3). Consensus scores were determined using the median. Inter-rater agreement was assessed using Fleiss' Kappa. Differences between models were analysed using Friedman and post hoc Wilcoxon signed-rank tests. Three hundred LLM recommendations were assessed. Inter-rater agreement was moderate (Fleiss' Kappa = 0.422, 95% CI 0.369-0.474). ChatGPT achieved the highest proportion of complete concordance with MDT recommendations (69%), followed by Meta AI (60%) and DeepSeek (53%). Clinically acceptable recommendations (scores 2-3) were produced in 93%, 88% and 89% of cases, respectively. Overall concordance differed significantly between models (Friedman χ
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