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clinical integration

Artificial Intelligence in Radiology: What Every Physician Should Know

Teaser: 

D'Arcy Little MD CCFP FCFP FRCPC,

D’Arcy Little, MD, CCFP, FCFP, FRCPC, Medical Director, Journal of Current Clinical Care and www.healthplexus.net, Radiologist, Orillia Soldiers’ Memorial Hospital, Adjunct Clinical Lecturer, Departments of Medical Imaging and Family Medicine, University of Toronto, Toronto, ON.

CLINICAL TOOLS

Abstract:
Artificial intelligence is rapidly being integrated into radiology practice, with over 1,000 AI-enabled devices now authorized by regulatory agencies worldwide. For referring physicians, understanding AI’s role in medical imaging is increasingly important, as these technologies affect report turnaround times, diagnostic accuracy, and clinical workflows. This article provides a practical overview of current AI applications in radiology, including automated detection of critical findings like pulmonary embolism and intracranial hemorrhage, enhanced cancer screening, and workflow optimization. AI tools assist radiologists in identifying abnormalities that might be missed, quantifying disease burden, and prioritizing urgent cases. However, AI augments rather than replaces radiologist expertise, and all findings require human verification. Future applications will extend beyond diagnosis to include predictive analytics, personalized imaging protocols, and integration with other clinical data sources. Physicians should understand that while AI enhances radiology services, it does not eliminate the need for appropriate clinical context in imaging requests or careful correlation of imaging findings with patient presentation. As AI adoption accelerates, collaboration between referring physicians and radiologists remains essential for optimal patient care.

Key Words: Artificial Intelligence (AI), radiology workflow, diagnostic accuracy, clinical integration.
AI enhances but doesn’t replace radiologists—Over 750 AI-enabled radiology devices are now approved, but they serve as assistive tools that flag findings for radiologist review rather than making final diagnoses. All AI findings require human verification and expert interpretation.
Faster detection of critical findings—AI algorithms can identify urgent conditions like pulmonary embolism, intracranial hemorrhage, and pneumothorax within seconds, enabling prioritization and reducing time-to-diagnosis for critical findings by 30-50% in some institutions.
Improved cancer screening performance—AI assistance increases mammography screening detection rates from 92% to 97%, helping identify small cancers that might otherwise be missed and providing consistent attention to every image.
Clinical context remains essential—Despite AI capabilities, detailed clinical information on imaging requisitions is still crucial, as AI analyzes image patterns but doesn’t understand clinical context the way radiologists do when interpreting findings.
Continue providing comprehensive clinical history—AI doesn’t diminish the importance of detailed clinical information on requisitions. Your description of symptoms, relevant history, and specific clinical questions directly impacts the radiologist’s ability to provide useful interpretations, even with AI support.
Don’t assume all findings are AI-detected—Most radiology reports don’t specifically mention AI use. The radiologist synthesizes information from multiple sources to create comprehensive interpretations, so maintain the same level of clinical correlation with imaging findings as you always have.
Communicate directly for complex cases—If imaging findings don’t match your clinical expectations, contact the radiologist directly. This person-to-person communication remains valuable for complex cases regardless of AI involvement and ensures optimal patient care.
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Related Terms: Radiology, artificial intelligence, radiology workflow, diagnostic accuracy, clinical integration
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