Radiology Reimagined: Advancing Clinical Practice Through AI Innovation (2025)-"AI Applications and Use Cases in Radiology: a 360° Overview"

"AI Applications and Use Cases in Radiology: a 360° Overview" – Dr. Amine Korchi

Pdf Summary

This comprehensive overview by Dr. Amine Korchi presents the current landscape, applications, and clinical integration of Artificial Intelligence (AI) in radiology. AI is rapidly transforming radiology workflows by supporting tasks ranging from exam selection and scheduling through image acquisition, interpretation, and reporting.<br /><br />Studies show growing adoption; nearly half of surveyed European radiologists currently use AI, with a significant portion employing it daily and finding it decisively helpful. AI fundamentally performs image-related functions such as segmentation, detection, classification, measurement, denoising, and also natural language processing for report generation and order structuring.<br /><br />AI aids non-interpretative tasks consuming 40-50% of radiologists’ time—like protocoling, quality assurance, communication, and administrative duties—thus reducing workflow interruptions and burnout. AI tools assist in exam selection by structuring orders, summarizing records, and safety screening, e.g., MRI pacemaker detection with high accuracy, though expert validation remains essential.<br /><br />In appointment management, AI chatbots handle scheduling and reminders, predict no-shows (with caution about bias), and optimize staffing based on workload forecasts. AI-enhanced image acquisition improves patient positioning, protocol selection, dose reduction (up to 75% radiation reduction in chest CT), and automated planning—boosting efficiency and consistency.<br /><br />Image interpretation AI includes detection, segmentation, quantification, risk prediction, and change monitoring. More than 600 FDA or CE-marked products are commercially available, mainly targeting chest, neuro, CT, and MRI imaging. Nonetheless, validation is often limited; few studies are prospective or multi-center randomized trials.<br /><br />Clinical trial evidence shows AI can safely reduce radiologist workload in mammography, improve lung nodule detection on chest X-rays, and facilitate opportunistic screening (e.g., hepatic steatosis). AI-assisted reporting reduces time, enhances patient-friendly communication, and maintains diagnostic accuracy with minimal errors.<br /><br />Key takeaways: AI is deeply integrated across the radiology value chain, improving diagnostic accuracy, efficiency, and workload, though robust validation remains an ongoing challenge. Non-interpretative AI and reporting augmentation represent promising frontiers for sustained productivity gains. AI is poised to remain a core element in radiology practice.

Keywords

Artificial Intelligence

Radiology

Image Acquisition

Image Interpretation

Workflow Optimization

AI in Clinical Practice

Radiology Reporting

Dose Reduction

AI Validation

Medical Imaging