Human-AI collaboration to identify and correct perceptual errors in chest
radiographs has not been previously explored. This study aimed to develop a
collaborative AI system, CoRaX, which integrates eye gaze data and radiology
reports to enhance diagnostic accuracy in chest radiology by pinpointing
perceptual errors and refining the decision-making process. Using public
datasets REFLACX and EGD-CXR, the study retrospectively developed CoRaX,
employing a large multimodal model to analyze image embeddings, eye gaze data,
and radiology reports. The system's effectiveness was evaluated based on its
referral-making process, the quality of referrals, and performance in
collaborative diagnostic settings. CoRaX was tested on a simulated error
dataset of 271 samples with 28% (93 of 332) missed abnormalities. The system
corrected 21% (71 of 332) of these errors, leaving 7% (22 of 312) unresolved.
The Referral-Usefulness score, indicating the accuracy of predicted regions for
all true referrals, was 0.63 (95% CI 0.59, 0.68). The Total-Usefulness score,
reflecting the diagnostic accuracy of CoRaX's interactions with radiologists,
showed that 84% (237 of 280) of these interactions had a score above 0.40. In
conclusion, CoRaX efficiently collaborates with radiologists to address
perceptual errors across various abnormalities, with potential applications in
the education and training of novice radiologists.

CoRaX 是一种协同人工智能系统，通过整合眼动数据和放射学报告来改进胸部放射学的诊断准确性，找出感知错误并优化决策过程。研究使用 REFLACX 和 EGD-CXR 数据集以及大型多模态模型开发了 CoRaX，并评估了其推荐过程、推荐质量和协同诊断能力。结果表明，CoRaX 能够高效地协同放射科医生，纠正各种异常中的感知错误，并在新手放射科医生的教育和培训中具有潜在应用。