Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration
Journal:
arXiv
Published Date:
May 11, 2025
Abstract
Generalist Medical AI (GMAI) systems have demonstrated expert-level
performance in biomedical perception tasks, yet their clinical utility remains
limited by inadequate multi-modal explainability and suboptimal prognostic
capabilities. Here, we present XMedGPT, a clinician-centric, multi-modal AI
assistant that integrates textual and visual interpretability to support
transparent and trustworthy medical decision-making. XMedGPT not only produces
accurate diagnostic and descriptive outputs, but also grounds referenced
anatomical sites within medical images, bridging critical gaps in
interpretability and enhancing clinician usability. To support real-world
deployment, we introduce a reliability indexing mechanism that quantifies
uncertainty through consistency-based assessment via interactive
question-answering. We validate XMedGPT across four pillars: multi-modal
interpretability, uncertainty quantification, and prognostic modeling, and
rigorous benchmarking. The model achieves an IoU of 0.703 across 141 anatomical
regions, and a Kendall's tau-b of 0.479, demonstrating strong alignment between
visual rationales and clinical outcomes. For uncertainty estimation, it attains
an AUC of 0.862 on visual question answering and 0.764 on radiology report
generation. In survival and recurrence prediction for lung and glioma cancers,
it surpasses prior leading models by 26.9%, and outperforms GPT-4o by 25.0%.
Rigorous benchmarking across 347 datasets covers 40 imaging modalities and
external validation spans 4 anatomical systems confirming exceptional
generalizability, with performance gains surpassing existing GMAI by 20.7% for
in-domain evaluation and 16.7% on 11,530 in-house data evaluation. Together,
XMedGPT represents a significant leap forward in clinician-centric AI
integration, offering trustworthy and scalable support for diverse healthcare
applications.
