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

Source: [MedXplain-VQA: Multi-Component Explainable Medical Visual Question Answering](<https://www.sota2.com/research/paper/69004e1e3ef7ccd74e5e2f48>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-29T05:05:10.050Z

Authors: Hai-Dang Nguyen, Minh-Anh Dang, Minh-Tan Le, Minh-Tuan Le

Published: 2025-10-26T19:23:20.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.22803>)

[PDF](<https://arxiv.org/pdf/2510.22803.pdf>)