Chest X-rays gain new screening potential through AI

AI is extending the clinical value of chest X-rays beyond lung and heart assessment. Researchers are investigating whether routine radiographs can support broader disease screening without the need for additional scans. Early findings suggest existing images may contain underused diagnostic signals.

A study in Radiology: Cardiothoracic Imaging examined whether AI could detect hepatic steatosis from standard frontal chest X-rays. Researchers analysed more than 6,500 images from over 4,400 patients across two institutions. Deep learning models were trained and externally validated.

The AI system achieved area-under-curve scores above 0.8 in both internal and external tests. Saliency maps showed predictions focused near the diaphragm, where part of the liver appears on chest X-rays. Results suggest that reliable signal extraction can be achieved from routine imaging.

Researchers argue the approach could enable opportunistic screening during standard care. Patients flagged by AI could be referred for a dedicated liver assessment when appropriate. The method adds clinical value without increasing imaging costs or radiation exposure.

Experts caution that the model is not a standalone diagnostic tool and requires further prospective validation. Integration with clinical and laboratory data remains necessary to reduce false positives. If validated, AI-enhanced X-rays could support scalable risk stratification.

Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our Diplo chatbot!