CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays
#CXReasonAgent #Chest X-ray diagnostics #AI medical imaging #Evidence-grounded reasoning #Large vision-language models #Clinical AI #Multi-turn dialogue benchmark #Medical AI reliability
📌 Key Takeaways
- Researchers developed CXReasonAgent to address limitations in current AI diagnostic models
- The system integrates language models with clinically grounded diagnostic tools
- They created CXReasonDial benchmark with 1,946 dialogues across 12 diagnostic tasks
- The new approach produces more faithfully grounded responses than conventional models
- This innovation is particularly important for safety-critical clinical environments
📖 Full Retelling
Researchers Hyungyung Lee, Hangyul Yoon, and Edward Choi introduced CXReasonAgent, a new diagnostic reasoning agent for chest X-rays, on February 26, 2026, addressing critical limitations in large vision-language models that have hindered their reliable application in clinical settings. The AI system integrates large language models with clinically grounded diagnostic tools to perform evidence-based reasoning using image-derived diagnostic and visual evidence, addressing a major challenge where existing models generate plausible but diagnostically ungrounded responses. To validate their approach, the researchers developed CXReasonDial, a comprehensive benchmark featuring 1,946 multi-turn dialogues across 12 diagnostic tasks, demonstrating that CXReasonAgent produces more faithfully grounded responses than conventional LVLMs. This innovation is particularly significant in safety-critical medical environments where diagnostic accuracy and verifiability can directly impact patient outcomes, potentially establishing a new standard for AI-assisted medical imaging analysis that balances sophisticated reasoning with clinical reliability.
🏷️ Themes
Medical AI, Diagnostic reasoning, Evidence-based medicine
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.23276 [Submitted on 26 Feb 2026] Title: CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays Authors: Hyungyung Lee , Hangyul Yoon , Edward Choi View a PDF of the paper titled CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays, by Hyungyung Lee and 2 other authors View PDF HTML Abstract: Chest X-ray plays a central role in thoracic diagnosis, and its interpretation inherently requires multi-step, evidence-grounded reasoning. However, large vision-language models often generate plausible responses that are not faithfully grounded in diagnostic evidence and provide limited visual evidence for verification, while also requiring costly retraining to support new diagnostic tasks, limiting their reliability and adaptability in clinical settings. To address these limitations, we present CXReasonAgent, a diagnostic agent that integrates a large language model with clinically grounded diagnostic tools to perform evidence-grounded diagnostic reasoning using image-derived diagnostic and visual evidence. To evaluate these capabilities, we introduce CXReasonDial, a multi-turn dialogue benchmark with 1,946 dialogues across 12 diagnostic tasks, and show that CXReasonAgent produces faithfully grounded responses, enabling more reliable and verifiable diagnostic reasoning than LVLMs. These findings highlight the importance of integrating clinically grounded diagnostic tools, particularly in safety-critical clinical settings. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.23276 [cs.AI] (or arXiv:2602.23276v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.23276 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hyungyung Lee [ view email ] [v1] Thu, 26 Feb 2026 17:51:21 UTC (18,344 KB) Full-text links: Access Paper: View a PDF of the paper titled CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agen...
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