RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate clinicians to passive observers of final outputs, offering no interpretable reasoning trace for them to inspect, validate, or refine. To address this, we introduce RadAgent, a tool-using AI agent that generates CT reports through a stepwise and interpretable process. Each resulting report is accompanied by a fully inspectable trace of intermediate decisions and tool interactions, allowing clinicians to examine how the reported findings are derived. In our experiments, we observe that RadAgent improves Chest CT report generation over its 3D VLM counterpart, CT-Chat, across three dimensions. Clinical accuracy improves by 6.0 points (36.4% relative) in macro-F1 and 5.4 points (19.6% relative) in micro-F1. Robustness under adversarial conditions improves by 24.7 points (41.9% relative). Furthermore, RadAgent achieves 37.0% in faithfulness, a new capability entirely absent in its 3D VLM counterpart. By structuring the interpretation of chest CT as an explicit, tool-augmented and iterative reasoning trace, RadAgent brings us closer toward transparent and reliable AI for radiology.
69e203a4ab10e63d135ae419
Mélanie Roschewitz
69e203a4ab10e63d135ae41a
Kenneth Styppa
69e203a4ab10e63d135ae41b
Yitian Tao
69e203a4ab10e63d135ae41c
Jiwoong Sohn
69e203a4ab10e63d135ae41d
Jean-Benoit Delbrouck
69e203a4ab10e63d135ae41e
Benjamin Gundersen
69e203a4ab10e63d135ae41f
Nicolas Deperrois
69e203a4ab10e63d135ae420
Christian Bluethgen
69e203a4ab10e63d135ae421
Julia Vogt
69e203a4ab10e63d135ae422
Bjoern Menze
69e203a4ab10e63d135ae423
Farhad Nooralahzadeh
69e203a4ab10e63d135ae424
Michael Krauthammer
69e203a4ab10e63d135ae425
Michael Moor
RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate clinicians to passive observers of final outputs, offering no interpretable reasoning trace for them to inspect, validate, or refine. To address this, we introduce RadAgent, a tool-using AI agent that generates CT reports through a stepwise and interpretable process. Each resulting report is accompanied by a fully inspectable trace of intermediate decisions and tool interactions, allowing clinicians to examine how the reported findings are derived. In our experiments, we observe that RadAgent improves Chest CT report generation over its 3D VLM counterpart, CT-Chat, across three dimensions. Clinical accuracy improves by 6.0 points (36.4% relative) in macro-F1 and 5.4 points (19.6% relative) in micro-F1. Robustness under adversarial conditions improves by 24.7 points (41.9% relative). Furthermore, RadAgent achieves 37.0% in faithfulness, a new capability entirely absent in its 3D VLM counterpart. By structuring the interpretation of chest CT as an explicit, tool-augmented and iterative reasoning trace, RadAgent brings us closer toward transparent and reliable AI for radiology.
RadAgent, a tool-using AI agent, enhances chest CT report generation through interpretable step-by-step reasoning traces that improve clinical accuracy, robustness, and faithfulness compared to existing 3D vision-language models.
Vision-language models CT reports tool-using AI agent interpretability reasoning trace clinical accuracy robustness faithfulness