confidence calibration

New Method Improves Confidence Calibration for Medical Multimodal LLMs by 40%

A new study presents the first comprehensive analysis of confidence calibration in medical multimodal large language models (MLLMs). The proposed method, combining Multi-Strategy Fusion-Based Interrogation (MS-FBI) with auxiliary expert LLM assessment, reduces Expected Calibration Error by an average of 40% across three Medical Visual Question Answering datasets, improving reliability for AI-assisted diagnosis.