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。

增强放射诊断：融合人工智能与人类专业知识的协作方法修正可视错误

Enhancing Radiological Diagnosis: A Collaborative Approach Integrating  AI and Human Expertise for Visual Miss Correction

Modern autonomous systems rely on perception modules to process complex
sensor measurements into state estimates. These estimates are then passed to a
controller, which uses them to make safety-critical decisions. It is therefore
important that we design perception systems to minimize errors that reduce the
overall safety of the system. We develop a risk-driven approach to designing
perception systems that accounts for the effect of perceptual errors on the
performance of the fully-integrated, closed-loop system. We formulate a risk
function to quantify the effect of a given perceptual error on overall safety,
and show how we can use it to design safer perception systems by including a
risk-dependent term in the loss function and generating training data in
risk-sensitive regions. We evaluate our techniques on a realistic vision-based
aircraft detect and avoid application and show that risk-driven design reduces
collision risk by 37% over a baseline system.

本文提出了一种基于风险分析的方法来设计自主系统感知模块，从而最小化感知误差对系统安全性的影响，并在真实的基于视觉的飞机避碰应用中证明，此方法可以将风险降低 37% 以上。