MedQwen-VGR1: Multimodal Vision-Guided Reasoning Model for Med-VQA, Temporal Diagnosis and Drug Interaction Analysis in Medical Imaging and Pathology

Abstract

MedQwen-VGR1 is a novel multimodal visionlanguage model (VLM) trained for medical visual question answering (Med-VQA), longitudinal temporal diagnosis, and drug interaction analysis across radiology and pathology imagery. The model undergoes a multi-stage training pipeline comprising: (1) Continuous Domain-Adaptive Pretraining on heterogeneous medical image-text corpora; (2) Supervised FineTuning on expert-annotated multimodal datasets spanning sequential imaging conversations; (3) Human Preference Alignment via Direct Preference Optimization (DPO) and Group Relative Preference Optimization (GRPO) applied to Vision-Guided Chain-of-Thought (CoT) Reasoning trajectories; and (4) Smart Memory Module integration enables Cross-Visit Context Retention and Comparative Reasoning on sequential or temporal pathologies. MedQwen-VGR1 processes sequential medical images and textual queries to generate stepwise, evidence-grounded rationales correlating visual features across timepoints, predict adverse drug interactions from images/metadata, and output calibrated reliability scores. Evaluations on PathVQA, SLAKE, and VQA-RAD demonstrate superior performance over LLaVA-Med (+14.7% accuracy) and BioViL-T (+12.3% temporal reasoning), achieving 82.4% VQA accuracy and 78.6% change detection precision, enabling multi-turn clinical dialogues and automated clinical reports. The framework enables dynamic, multi-turn clinical dialogues with automated medical report synthesis, addressing critical gaps in temporal reasoning, explainability, and clinical safety alignment for realworld AI and safety enhanced clinical decision support systems.