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arXiv · 2503.18562

Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models

Nariman Naderi·Seyed Amir Ahmad Safavi-Naini·Thomas Savage·Zahra Atf·Peter Lewis·Girish Nadkarni·Ali Soroush
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Abstract

This study evaluated self-reported response certainty across several large language models (GPT, Claude, Llama, Phi, Mistral, Gemini, Gemma, and Qwen) using 300 gastroenterology board-style questions. The highest-performing models (GPT-o1 preview, GPT-4o, and Claude-3.5-Sonnet) achieved Brier scores of 0.15-0.2 and AUROC of 0.6. Although newer models demonstrated improved performance, all exhibited a consistent tendency towards overconfidence. Uncertainty estimation presents a significant challenge to the safe use of LLMs in healthcare. Keywords: Large Language Models; Confidence Elicitation; Artificial Intelligence; Gastroenterology; Uncertainty Quantification

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Nariman Naderi, Seyed Amir Ahmad Safavi-Naini, Thomas Savage, Zahra Atf, Peter Lewis, Girish Nadkarni, Ali Soroush. 2025-03-24. Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models. https://doi.org/10.1038/s44355-026-00053-3

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