CentaurMD: Confidence-Aware Human-AI Decision Fusion for Multi-Label Disease Diagnosis via Label-Specific MoE

Multi-label disease diagnosis is prevalent in clinical applications, such as chest X-rays that may indicate multiple coexisting diseases. Despite advances in AI, current models remain insufficient for reliably addressing such complexity. Human-AI synergy thus emerges as both a necessary and promising approach, motivating our focus on effective decision fusion for multi-label disease diagnosis. There are two challenges. Confidence, a key factor in decision fusion, is often unrecorded in human annotations, making its estimation nontrivial. Moreover, label-specific variations in human and model expertise must be considered to achieve effective fusion. To address these challenges, we propose CentaurMD, a confidence-aware human-AI decision fusion framework based on label-specific Mixture-of-Experts (MoE). We first present a novel multi-label confusion matrix construction method that employs maximum entropy modeling to capture label correlations, enabling more accurate confidence estimation and weight allocation. Then, we develop a label-specific MoE module with dedicated gating networks and thresholds, which dynamically adjust expert weights using information extracted from the confusion matrix via a Transformer encoder. Extensive experiments on three real-world clinical datasets demonstrate that our method reduces Hamming loss by 39.14% and improves MMR (missed-misdiagnosis reduction) by 17.38%, achieving substantial diagnostic improvements.