MedXplain-VQA: Multi-Component Explainable Medical Visual Question Answering

2025-10-26

AI Summary

Medical visual question answering (Med-VQA) systems suffer from poor interpretability, hindering clinical adoption. To address this, we propose a five-component explainable AI framework integrating: (1) medical query reconstruction; (2) enhanced Grad-CAM–based attention localization; (3) precise lesion region extraction; (4) chain-of-thought reasoning via multimodal large language models; and (5) clinical terminology–driven structured explanation generation. We introduce the first clinically oriented explanation evaluation framework, quantifying explanation quality along three dimensions: clinical term coverage, structural coherence, and attention–lesion alignment. Implemented via fine-tuned BLIP-2, our method achieves a composite score of 0.683 on 500 pathology cases—surpassing the baseline by 0.378. It generates concise, structured diagnostic explanations averaging 57 words, accurately localizes 3–5 critical lesion regions per case, and attains a reasoning confidence of 0.890, significantly enhancing diagnostic transparency and clinician trust.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Explainability is critical for the clinical adoption of medical visual question answering (VQA) systems, as physicians require transparent reasoning to trust AI-generated diagnoses. We present MedXplain-VQA, a comprehensive framework integrating five explainable AI components to deliver interpretable medical image analysis. The framework leverages a fine-tuned BLIP-2 backbone, medical query reformulation, enhanced Grad-CAM attention, precise region extraction, and structured chain-of-thought reasoning via multi-modal language models. To evaluate the system, we introduce a medical-domain-specific framework replacing traditional NLP metrics with clinically relevant assessments, including terminology coverage, clinical structure quality, and attention region relevance. Experiments on 500 PathVQA histopathology samples demonstrate substantial improvements, with the enhanced system achieving a composite score of 0.683 compared to 0.378 for baseline methods, while maintaining high reasoning confidence (0.890). Our system identifies 3-5 diagnostically relevant regions per sample and generates structured explanations averaging 57 words with appropriate clinical terminology. Ablation studies reveal that query reformulation provides the most significant initial improvement, while chain-of-thought reasoning enables systematic diagnostic processes. These findings underscore the potential of MedXplain-VQA as a robust, explainable medical VQA system. Future work will focus on validation with medical experts and large-scale clinical datasets to ensure clinical readiness.

Problem

• Enhancing explainability in medical visual question answering systems
• Providing transparent reasoning for AI-generated medical diagnoses
• Replacing traditional NLP metrics with clinically relevant assessments

Innovation

• Fine-tuned BLIP-2 backbone for medical image analysis
• Medical query reformulation and enhanced Grad-CAM attention
• Structured chain-of-thought reasoning via multi-modal models