# Evaluating AI Models for Pneumothorax Detection on Chest Radiographs: Diagnostic Accuracy and Clinical Trade-Offs

## TL;DR

This paper evaluates AI models for detecting pneumothorax in chest X-rays, finding that they have potential but require careful use due to diagnostic trade-offs.

## Contribution

The study introduces a systematic evaluation of AI diagnostic performance for pneumothorax detection, highlighting clinical trade-offs between false positives and false negatives.

## Key findings

- One AI model achieved 64% diagnostic accuracy with moderate precision and recall.
- Another model showed high sensitivity (88%) but lower precision (55%).
- Balanced models may be better for screening, while high-sensitivity models suit triage.

## Abstract

Background

Pneumothorax is a critical condition where timely recognition on chest radiographs is essential, particularly in emergency and resource-limited settings. Emerging artificial intelligence (AI) systems capable of native image interpretation offer potential to augment clinical workflows, yet their diagnostic reliability remains underexplored.

Methods

We evaluated two state-of-the-art AI models on 2,000 publicly available frontal chest radiographs, equally divided between pneumothorax-positive and pneumothorax-negative cases. Models were prompted with standardized diagnostic instructions emphasizing pleural line visualization, asymmetry in lung translucency, and the deep sulcus sign. Predictions were assessed against reference diagnoses using accuracy, precision, recall, and F1 score.

Results

One model achieved balanced diagnostic accuracy (64%) with a precision of 66% and a recall of 57%, while the other demonstrated higher sensitivity (88%) but lower precision (55%). These divergent profiles underscore trade-offs between minimizing false negatives and limiting false positives.

Conclusions

AI systems show promise for pneumothorax detection on chest radiographs but exhibit distinct diagnostic biases that must be carefully matched to the clinical context. Balanced performance models may be suitable for general screening, whereas high-sensitivity models may better support triage workflows. Rigorous validation, integration strategies, and human supervision remain essential before deployment in real-world clinical practice.