Robustness of an Artificial Intelligence Solution for Diagnosis of Normal Chest X-Rays

TL;DR

This study evaluates the robustness of an AI system for diagnosing normal chest X-rays, demonstrating high negative predictive value and consistent performance across diverse patient groups and settings, with potential workload reduction benefits.

Contribution

The paper provides a comprehensive assessment of an AI diagnostic tool's robustness across multiple subgroups and compares its errors with human radiologists, highlighting its reliability and efficiency.

Findings

AI classified 18.5% of scans as high confidence normal

AI achieved a negative predictive value of 96.0%

AI errors matched radiologist errors in false negatives

Abstract

Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settings. Ground-truth labels were assigned by a 3-radiologist panel. AI performance was evaluated against assigned labels and sub-groups analysis was conducted against patient age and sex, as well as CXR view, modality, device manufacturer and hospital site. Results: The AI solution was able to remove 18.5% of the dataset by classification as High Confidence Normal (HCN). This was associated with a negative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of normal scans by radiologists. In all AI false negative (FN) cases, a radiologist was found to have also made the same error when compared to final ground-truth labels. Subgroup analysis showed no statistically significant variations in AI performance, whilst reduced normal classification was observed in data from some hospital sites. Conclusion: We show the AI solution could provide meaningful workload savings by diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The AI solution is shown to perform well across patient subgroups and error cases were shown to be subjective or subtle in nature.