mlsanityData to 13 Oct 2026

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

arXiv 2503.18562 ↗First posted 24 Mar 2025cs.CL cs.AI cs.HCcited by 3

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Abstract (arXiv)

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

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

16 of 27 references.

References · 16

  1. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,280
  2. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 960
  3. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 800
  4. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 798
  5. 2022Can large language models reason about medical questions?cited by 194
  6. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 169
  7. 2024Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantificationcited by 159
  8. 2023Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Modelscited by 149
  9. 2024Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraphcited by 105
  10. 2024On Verbalized Confidence Scores for LLMscited by 83
  11. 2023Skill-Mix: a Flexible and Expandable Family of Evaluations for AI modelscited by 64
  12. 2024The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs)cited by 45
4 more
  1. 2022Measuring Reliability of Large Language Models through Semantic Consistencycited by 38
  2. 2024Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planningcited by 16
  3. 2024Uncertainty Estimation of Large Language Models in Medical Question Answeringcited by 10
  4. 2024Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Modelscited by 7

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025The challenge of uncertainty quantification of large language models in medicinecited by 14
  2. 2025Trustworthy Medical Question Answering: An Evaluation-Centric Surveycited by 7
  3. 2026OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions via System Onecited by 0

Similar, not cited

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  1. 2025Susceptibility of Large Language Models to User-Driven Factors in Medical Queries
  2. 2025Measuring Stability Beyond Accuracy in Small Open-Source Medical Large Language Models for Pediatric Endocrinology
  3. 2026GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards
  4. 2024Can Large Language Models Logically Predict Myocardial Infarction? Evaluation based on UK Biobank Cohort
  5. 2023Methods to Estimate Large Language Model Confidence
  6. 2025Evaluating Large Language Models for Evidence-Based Clinical Question Answering
  7. 2026Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation
  8. 2025Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs