Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA

This study presents a novel method for improving confidence calibration in Multimodal Large Language Models (MLLMs) applied to Medical Visual Question Answering (VQA) by integrating Multi-Strategy Fusion-Based Interrogation (MS-FBI) with expert LLM assessments. The proposed approach achieved a 40% reduction in Expected Calibration Error (ECE) across three Medical VQA datasets, underscoring the need for domain-specific calibration to enhance the reliability of MLLMs in healthcare applications. This advancement is critical for practitioners as it aims to mitigate the risks of misdiagnosis and improve the trustworthiness of AI-assisted medical decisions.