Enhancing Radiological Diagnosis: A Collaborative Approach Integrating AI and Human Expertise for Visual Miss Correction
Project Overview
The document explores the innovative use of the Collaborative Radiology Expert (CoRaX) system, which combines AI technology with human expertise to enhance diagnostic accuracy in chest radiology. By leveraging eye gaze data and analyzing radiology reports, CoRaX effectively identifies and rectifies perceptual errors made by radiologists, leading to improved diagnostic performance. The findings demonstrate that CoRaX not only enhances the accuracy of diagnoses but also serves as a valuable educational tool, particularly for training novice radiologists. This dual application of CoRaX underscores its potential to transform both clinical practice and educational methodologies in the field of radiology, showcasing the significant benefits of integrating generative AI into medical education. Through its implementation, CoRaX exemplifies how generative AI can facilitate better learning outcomes and support the development of critical diagnostic skills in healthcare professionals.
Key Applications
Collaborative Radiology Expert (CoRaX)
Context: Educational use for training inexperienced radiologists and improving diagnostic accuracy in chest radiology.
Implementation: The system utilizes eye gaze data and radiology reports to identify perceptual errors and enhance decision-making processes.
Outcomes: The system corrected 21% of identified perceptual errors and achieved a Total-Usefulness score of 84% in aiding diagnostic performance.
Challenges: The system faces challenges in fully encompassing the spectrum of perceptual errors in genuine radiology reports and slight misalignment between eye gaze movements and report transcription.
Implementation Barriers
Technical Barrier
Inherent errors caused by misalignment between eye gaze movements and report transcription.
Proposed Solutions: Improving integration techniques and refining data collection methods for better alignment.
Data Limitations
The simulated error dataset may not fully represent the wide range of perceptual errors in actual clinical practice.
Proposed Solutions: Expanding training datasets and incorporating real-world error data to enhance system training.
Project Team
Akash Awasthi
Researcher
Ngan Le
Researcher
Zhigang Deng
Researcher
Carol C. Wu
Researcher
Hien Van Nguyen
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Akash Awasthi, Ngan Le, Zhigang Deng, Carol C. Wu, Hien Van Nguyen
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai