Explainability domain generalization and rare class reliability are critical challenges in medical AI where deep models often fail under real world distribution shifts and exhibit bias against infrequent clinical conditions This paper introduces XAIMeD an explainable medical AI framework that integrates clinically accurate expert knowledge into deep learning through a unified neuro symbolic architecture XAIMeD is designed to improve robustness under distribution shift enhance rare class sensitivity and deliver transparent clinically aligned interpretations The framework encodes clinical expertise as logical connectives over atomic medical propositions transforming them into machine checkable class specific rules Their diagnostic utility is quantified through weighted feature satisfaction scores enabling a symbolic reasoning branch that complements neural predictions A confidence weighted fusion integrates symbolic and deep outputs while a Hunt inspired adaptive routing mechanism guided by Entropy Imbalance Gain EIG and Rare Class Gini mitigates class imbalance high intra class variability and uncertainty We evaluate XAIMeD across diverse modalities on four challenging tasks i Seizure Onset Zone SOZ localization from rs fMRI ii Diabetic Retinopathy grading across 6 multicenter datasets demonstrate substantial performance improvements including 6 percent gains in cross domain generalization and a 10 percent improved rare class F1 score far outperforming state of the art deep learning baselines Ablation studies confirm that the clinically grounded symbolic components act as effective regularizers ensuring robustness to distribution shifts XAIMeD thus provides a principled clinically faithful and interpretable approach to multimodal medical AI.

翻译：可解释性领域泛化与罕见类别可靠性是医疗人工智能领域的关键挑战，深度学习模型在真实世界分布偏移下常出现失效，并对不常见的临床病症表现出检测偏差。本文提出XAI-MeD——一种可解释的医疗AI框架，通过统一的神经符号架构将临床精确的专家知识整合到深度学习中。XAI-MeD旨在提升模型在分布偏移下的鲁棒性、增强罕见类别敏感性，并提供透明且符合临床逻辑的决策解释。该框架将临床专业知识编码为原子医学命题的逻辑连接式，并将其转化为可机器验证的类别特定规则。通过加权特征满足度分数量化其诊断效用，构建出与神经预测分支互补的符号推理分支。置信度加权融合机制整合符号与深度输出，而受Hunt算法启发的自适应路由机制——以熵不平衡增益（EIG）与罕见类别基尼系数为引导——有效缓解了类别不平衡、高类内变异性和不确定性。我们在多模态数据上对XAI-MeD进行四项挑战性任务的评估：i）基于静息态功能磁共振成像（rs-fMRI）的癫痫发作起始区（SOZ）定位；ii）跨6个多中心数据集的糖尿病视网膜病变分级。实验表明，该框架取得显著性能提升：跨领域泛化能力提高6%，罕见类别F1分数提升10%，远超当前最先进的深度学习基线。消融研究证实，基于临床知识的符号组件可作为有效的正则化器，确保模型对分布偏移的鲁棒性。因此，XAI-MeD为多模态医疗AI提供了一种原理清晰、临床可信且可解释的研究路径。