Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA

2026-07-16

AI Summary

This work addresses the limitations of current medical multimodal AI systems, which often prioritize answer accuracy at the expense of clinical reasoning reliability and interpretability. Leveraging the MediaEval Medico 2025 gastrointestinal endoscopy dataset, the study systematically evaluates nine multimodal visual question answering (VQA) systems and introduces an evaluation paradigm that transcends conventional leaderboard metrics. The proposed framework emphasizes structured reasoning, explicit visual-textual evidence alignment, mechanisms to prevent data leakage, and lightweight robustness calibration. Experimental results demonstrate that merely improving accuracy does not necessarily enhance clinical trustworthiness; in contrast, approaches incorporating structured reasoning and explicit grounding exhibit greater robustness on heterogeneous clinical questions. These findings underscore the necessity and efficacy of the new evaluation dimensions in advancing trustworthy medical AI.

Abstract

Healthcare multimodal AI must combine visual and textual evidence while remaining reliable and interpretable. Using MediaEval Medico 2025 as a retrospective GI endoscopy case study, we analyze design choices across nine documented systems for question answering and explanation quality. Parameter-efficient adaptation of pretrained backbones provides strong challenge performance, but answer-level gains do not consistently translate into faithful and complete clinical reasoning. Methods enforcing structured reasoning and explicit grounding show more reliable behavior across heterogeneous question types, although the evidence is correlational rather than ablation-based. These results motivate evaluation beyond lexical overlap, standardized evidence-linked explanations, leakage-aware data governance, and lightweight robustness and calibration checks. The findings support trustworthy multimodal healthcare AI based on data fusion, explainability, and resilient evaluation.

Problem

• multimodal VQA
• trustworthy AI
• explainability
• clinical reasoning
• evidence grounding

Innovation

• trustworthy AI
• multimodal VQA
• structured reasoning
• evidence grounding
• explainability