Real-Time AI in Medical Imaging Diagnostics

Abstract

Artificial intelligence algorithms can now match or exceed radiologist-level accuracy on narrowly defined image-classification tasks, yet their integration into real-time clinical imaging workflows remains inconsistent across modalities and diagnostic applications. We evaluated 228 real-time AI medical imaging programmes deployed or piloted across radiology departments in France, Italy, and Austria between 2016 and 2024, spanning five modality categories: chest radiograph triage, mammographic screening augmentation, CT pulmonary embolism detection, retinal fundus screening, and ultrasound point-of-care assistance. A Real-Time AI Imaging Deployment Index (RTAIDI) was constructed from five sub-scores -- diagnostic accuracy in clinical conditions, inference latency, radiologist trust and adoption rate, PACS/workflow integration depth, and measurable patient outcome improvement -- with weights from regression against sustained operational deployment beyond 12 months. RTAIDI correlated with deployment retention at r = +0.84 and discriminated retained from discontinued programmes with an AUC of 0.884. Chest radiograph triage programmes scored highest (mean RTAIDI 0.824), while ultrasound point-of-care trailed at 0.596. Only 35.5 percent of programmes exceeded the 0.75 threshold. Diagnostic accuracy carried the largest regression weight (beta = +0.280), followed by radiologist trust (beta = +0.226).