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[2602.23276] CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays
Summary
The CXReasonAgent integrates large language models with diagnostic tools for improved reasoning in chest X-ray interpretations, addressing limitations of existing models.
Why It Matters
This research is significant as it enhances the reliability and adaptability of AI in clinical settings, particularly in diagnosing thoracic conditions. By providing evidence-grounded reasoning, it aims to improve patient outcomes and safety in healthcare.
Key Takeaways
- CXReasonAgent combines LLMs with diagnostic tools for better accuracy.
- It addresses the shortcomings of existing large vision-language models.
- CXReasonDial benchmark evaluates the model's performance across various tasks.
- The integration of clinical tools is crucial for safety in medical diagnostics.
- The findings could lead to more reliable AI applications in healthcare.
Computer Science > Artificial Intelligence arXiv:2602.23276 (cs) [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 (experimental) 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 (LVLMs) 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 (LLM) 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 di...