Collaborative Integration of AI and Human Expertise to Improve Detection of Chest Radiograph Abnormalities.
Authors
Affiliations (5)
Affiliations (5)
- Department of Electrical and Computer Engineering, University of Houston, 4222 Martin Luther King Blvd, Cullen College of Engineering Building-1, Rm N368, Houston, TX 77204.
- Department of Computer Science & Computer Engineering, University of Arkansas, Fayetteville, Ark.
- Department of Computer Science, University of Houston, Houston, Tex.
- Department of Thoracic Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex.
- Department of Electrical and Computer Engineering, University of Houston, Houston, Tex.
Abstract
<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a collaborative AI system that integrates eye gaze data and radiology reports to improve diagnostic accuracy in chest radiograph interpretation by identifying and correcting perceptual errors. Materials and Methods This retrospective study utilized public datasets REFLACX and EGD-CXR to develop a collaborative AI solution, named Collaborative Radiology Expert (CoRaX). It employs a large multimodal model to analyze image embeddings, eye gaze data, and radiology reports, aiming to rectify perceptual errors in chest radiology. The proposed system was evaluated using two simulated error datasets featuring random and uncertain alterations of five abnormalities. Evaluation focused on the system's referral-making process, the quality of referrals, and its performance within collaborative diagnostic settings. Results In the random masking-based error dataset, 28.0% (93/332) of abnormalities were altered. The system successfully corrected 21.3% (71/332) of these errors, with 6.6% (22/332) remaining unresolved. The accuracy of the system in identifying the correct regions of interest for missed abnormalities was 63.0% [95% CI: 59.0%, 68.0%], and 85.7% (240/280) of interactions with radiologists were deemed satisfactory, meaning that the system provided diagnostic aid to radiologists. In the uncertainty-masking-based error dataset, 43.9% (146/332) of abnormalities were altered. The system corrected 34.6% (115/332) of these errors, with 9.3% (31/332) unresolved. The accuracy of predicted regions of missed abnormalities for this dataset was 58.0% [95% CI: 55.0%, 62.0%], and 78.4% (233/297) of interactions were satisfactory. Conclusion The CoRaX system can collaborate efficiently with radiologists and address perceptual errors across various abnormalities in chest radiographs. ©RSNA, 2025.