ArticleJMIR cancer2026
Evaluation of GPT-5 for Esophageal Cancer Staging Using Fluorodeoxyglucose Positron Emission Tomography Maximum-Intensity Projection Images: Comparative Pilot Study.
Article in JMIR cancer, 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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Abstract
backgroundAccurate esophageal cancer staging relies on
objectiveWe evaluated the diagnostic accuracy of LLMs for staging esophageal cancer using
methodsThis retrospective study included 120 consecutive adult patients who were diagnosed with esophageal squamous cell carcinoma and underwent
resultsThe average accuracy was 41/120 (34%) to 94/120 (78%) for LLMs and 72/120 (60%) to 102/120 (85%) for physicians, with significantly higher accuracy for physicians (P<.05) in the thoracic LN, abdominal LN, and cN stages. Interrater reliability was slight to fair for LLMs (κ: -0.07 to 0.25) and fair to substantial for physicians (κ: 0.27 to 0.74). Matthews Correlation Coefficient scores were consistently higher for physicians (0.28 to 0.75) than for LLMs (-0.07 to 0.32). Among the LLMs, GPT-5 demonstrated the highest overall accuracy, with newer LLMs showing improved diagnostic accuracy when compared with previous models in identifying abdominal LN metastases and cM staging, though they showed weaker consistency for cN staging. For example, in thoracic LN detection, GPT-5 achieved 76/120 (63%) accuracy, whereas other LLMs achieved 72/120 (60%) or lower accuracy.
conclusionsAlthough current LLMs have not yet reached physician-level accuracy in comprehensive staging, recent models show promise in assisting with specific diagnostic tasks.
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