Use of Artificial Intelligence in Radiology and Regulatory Standards

Artificial intelligence in radiology refers to computer vision and machine learning algorithms trained to acquire, optimize, analyze, and triage diagnostic medical images. Functioning as clinical decision support rather than replacing experts, these software programs detect anomalies under human physician supervision, perform structural measurements, and prioritize urgent cases.

Radiology artificial intelligence applications consist of deep neural networks trained to process diagnostic images in modalities such as X-ray, computed tomography, magnetic resonance imaging, and ultrasound. In clinical workflows, this software operates in compliance with the DICOM standard. Once imaging is complete, the software reads the series, extracts features, and produces quantitative outputs such as bounding boxes around suspicious findings, lesion segmentations, or worklist flags. According to consensus statements from professional imaging societies, clinical AI serves as an auxiliary technology, and licensed physicians bear sole responsibility for interpreting findings and signing the final report.

The applied process consists of a specific operational pipeline within imaging environments. During the image acquisition guidance phase, under FDA regulation 21 CFR 892.2100, AI-enabled systems assist technologists with patient centering, reducing motion artifacts, and optimizing radiation dose protocols. In the data acquisition and pre-processing phase, images are transmitted to archive systems, and an integration gateway routes the series to the inference engine.

During the algorithmic inference phase, deep learning models perform inferences appropriate for clinical indications; for example, in acute protocols, triage algorithms evaluate head CT scans or angiograms within minutes. In the structured reporting and worklist tagging steps, results are packaged as DICOM objects, and priority alerts are sent to worklists to ensure emergencies are reviewed prior to routine studies. In the physician review and lifecycle monitoring phase, model performance is continuously monitored under the Good Machine Learning Practice principles established by the FDA, Health Canada, and the UK MHRA.