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Tracked in 178 AFBytes stories. First seen May 27, 2026. Last seen Jun 03, 2026.

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Confidence Measurement Metrics in Multimodal Large Language Models for Ultrasound-Based Radiology Cases: Comparative Evaluation Study of Self-Reported, Consistency-Based, and Hybrid Methods
jmir.org · Jun 2, 2026 22:11 UTC

Confidence Measurement Metrics in Multimodal Large Language Models for Ultrasound-Based Radiology Cases: Comparative Evaluation Study of Self-Reported, Consistency-Based, and Hybrid Methods

Background: Large language models (LLMs) require specialized methodologies to quantify model confidence for safe deployment in health care systems; however, there is a lack of established methods for confidence assessment. Objective: This study aimed to evaluate confidence metrics for multimodal LLMs interpreting ultrasound-based radiology cases and to compare self-reported, consistency-based, and hybrid methods. Methods: From a total of 330 quizzes on the Korean Society of Ultrasound in Medicine digital platform, we selected 94 multiple-choice cases. Four multimodal LLMs were evaluated: 3 reasoning models (GPT-5, Claude-4.5-Sonnet, and Gemini-3-Pro) and 1 general model (GPT-4o). Temperature was fixed at 1.0. Multiple confidence metrics were assessed: (1) self-reported metrics generated by LLMs using prompts that elicited direct confidence percentages with answers, including first self-reported confidence and mean self-reported confidence; (2) consistency-based metrics derived from 20 repeated outputs per case, including relative entropy calculated as 1 − H/log k (H=Shannon entropy, k=number of answer choices) and majority-vote percentage; and (3) a Top Weighted Score combining response frequency with self-reported confidence. Receiver operating characteristic analysis for discrimination and Spearman correlation between accuracy and each confidence metric was conducted. Additionally, model calibration was assessed using expected calibration error and Brier score. Processing time and token consumption (input, output, and total) were recorded for each application programming interface call to evaluate resource use across models. Results: Diagnostic accuracy varied across models, with Gemini-3-Pro achieving the highest accuracy (70/94, 74.47%), surpassing the median human accuracy (59%, IQR 40.3%-75%). Top Weighted Score, a hybrid metric combining response frequency and self-reported confidence, was the only metric achieving statistically significant correlations across all 4 models: Gemini-3-Pro (ρ=0.52), GPT-5 (ρ=0.43), Claude-4.5-Sonnet (ρ=0.30), and GPT-4o (ρ=0.22). Receiver operating characteristic analysis revealed that Top Weighted Score demonstrated the highest discriminative ability, with area under the curve values of 0.826 (95% CI 0.731‐0.920) for Gemini-3-Pro and 0.767 (95% CI 0.668‐0.866) for GPT-5. Top Weighted Score was the only metric achieving statistical significance in GPT-4o. Calibration analysis showed that Top Weighted Score achieved the lowest expected calibration error in GPT-5 (0.098) and Claude-4.5-Sonnet (0.192), while Gemini-3-Pro showed comparable calibration between relative entropy (0.119) and Top Weighted Score (0.122). Resource use analysis demonstrated that reasoning models required substantially longer processing times and higher token consumption compared to general models. Conclusions: In multimodal LLMs applied to ultrasound-based radiology cases, hybrid methods (Top Weighted Score) demonstrated significant associations across all evaluated models and appear to serve as more reliable indicators of diagnostic confidence compared to self-reported or consistency-based metrics alone, although the strength of these associations varied across models, and external validation is warranted before broader clinical application. These findings support integrative confidence estimation approaches that incorporate response consistency while highlighting the need for resource-efficient sampling strategies to enable practical clinical deployment.

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