Best Clinical AI Tools for Radiology

The average radiologist interprets between 50 and 100 studies per day, with some high-volume practices pushing well beyond that. The cognitive demands are enormous: each study requires integration of imaging findings, clinical history, prior studies, and relevant literature. Burnout rates in radiology are among the highest in medicine (Medscape Physician Burnout Report, 2024). Where CDS tools provide the most value to radiologists is not in image interpretation (that's the domain of AI-assisted detection tools like Aidoc, Viz.ai, and Qure.ai), but in clinical decision-making around imaging. The ACR Appropriateness Criteria guide whether an imaging study should be ordered in the first place — critical for radiologists who review orders and consult on imaging protocols. Incidental finding management is another major pain point: the Fleischner criteria for pulmonary nodules, ACR recommendations for adrenal incidentalomas, and Bosniak classification for renal cysts all require evidence-based follow-up algorithms that are difficult to memorize across all organ systems. Contrast safety represents a third important decision point. Gadolinium-based contrast agents in patients with renal impairment, iodinated contrast in patients with thyroid disease or metformin use, and allergic reaction management protocols all require current evidence. Our radiology evaluation focuses on these practical decision points rather than image interpretation assistance.