# Diagnostic Accuracy of a Multi-Target Artificial Intelligence Service for the Simultaneous Assessment of 16 Pathological Features on Chest and Abdominal CT

## TL;DR

This study evaluates an AI system's ability to detect 16 pathological features in chest and abdominal CT scans, finding it performs well overall except for detecting kidney stones.

## Contribution

The study introduces a multi-target AI service for simultaneous assessment of multiple pathological features in CT scans, showing high diagnostic accuracy.

## Key findings

- The AI service achieved an overall AUC of 0.88 for detecting 16 pathological features in CT scans.
- Most AI errors were classified as minor or intermediate, with only 5.4% being clinically significant.
- The AI performed poorly for urolithiasis detection compared to other features.

## Abstract

Background/Objectives: Chest, abdominal, and pelvic computed tomography (CT) with intravenous contrast is widely used for tumor staging, treatment planning, and therapy monitoring. The integration of artificial intelligence (AI) services is expected to improve diagnostic accuracy across multiple anatomical regions simultaneously. We aimed to evaluate the diagnostic accuracy of a multi-target AI service in detecting 16 pathological features on chest and abdominal CT images. Methods: We conducted a retrospective study using anonymized CT data from an open dataset. A total of 229 CT scans were independently interpreted by four radiologists with more than 5 years of experience and analyzed by the AI service. Sixteen pathological features were assessed. AI errors were classified as minor, intermediate, or clinically significant. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Across 229 CT scans, the AI service made 423 errors (11.5% of all evaluated features, n = 3664). False positives accounted for 262 cases (61.9%) and false negatives for 161 (38.1%). Most errors were minor (62.9%) or intermediate (31.7%), while clinically significant errors comprised only 5.4%. The overall AUC of the AI service was 0.88 (95% CI: 0.87–0.89), compared with 0.78–0.81 for radiologists. For clinically significant findings, the AI AUC was 0.90 (95% CI: 0.71–1.00). Diagnostic accuracy was unsatisfactory only for urolithiasis. Conclusions: The multi-target AI service demonstrated high diagnostic accuracy for chest and abdominal CT interpretation, with most errors being clinically negligible; performance was limited for urolithiasis.