ArticleCommunications medicine2026
Advancing radiology foundation models with reasoning through step-by-step verification from daily reports.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Foundation models in medical image analysis: A systematic review and quantitative analysis.Medical image analysis · 2026Review
- Reinforcement learning improves LLM accuracy and reasoning in disease classification from radiology reports.NPJ digital medicine · 2026Article
- Evidence-Guided Diagnostic Reasoning for Pediatric Chest Radiology Based on Multimodal Large Language Models.Journal of imaging · 2026Article
- Evaluating AI Models for Pneumothorax Detection on Chest Radiographs: Diagnostic Accuracy and Clinical Trade-Offs.Cureus · 2025Article
- Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.BJR open · 2025Review
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Authors and funding
6 authors.
Funding
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Abstract
backgroundRecent advances in reasoning-enhanced large language models (LLMs) and multimodal LLMs (MLLMs) have significantly improved performance in complex tasks, yet medical AI models often overlook the structured reasoning processes inherent in clinical practice.
methodsIn this work, we propose a two-stage pipeline to train ChestX-Reasoner, a radiology diagnosis MLLM with process supervision mined directly from clinical reports, reflecting the step-by-step reasoning followed by radiologists. To facilitate and evaluate reasoning capabilities, we introduce RadRBench-CXR, a comprehensive benchmark featuring 59K visual question answering samples with 301K clinically validated reasoning steps, and RadRScore, a metric evaluating reasoning factuality, completeness, and effectiveness.
resultsHere we show that, ChestX-Reasoner achieves significant improvements of 16% and 8.5% in reasoning ability compared to leading medical and general-purpose models, respectively. Furthermore, diagnostic accuracy improves by 3.3% to 24% against baselines.
conclusionsThese results demonstrate that incorporating explicit reasoning steps improves diagnostic outcomes, and integrating process supervision enhances the reliability and transparency of medical diagnosis.
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Registered trials
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