In Depth
Computer-Aided Diagnosis (CAD) systems use AI, particularly deep learning and computer vision, to help clinicians detect and diagnose diseases from medical data. Modern CAD systems analyze X-rays, CT scans, MRIs, pathology slides, retinal images, and other medical data to identify abnormalities that might indicate conditions like cancer, fractures, diabetic retinopathy, and cardiovascular disease.
AI-powered CAD systems have achieved radiologist-level accuracy in several specific tasks, including detecting breast cancer in mammograms, identifying lung nodules in CT scans, and diagnosing diabetic retinopathy from retinal photos. These systems serve as a 'second pair of eyes,' reducing missed diagnoses and improving consistency. They do not replace physicians but augment their capabilities, particularly in settings with limited specialist access.
The regulatory landscape for CAD is evolving rapidly. The FDA has approved hundreds of AI-based medical devices, with new frameworks emerging for continuous learning systems that improve over time. For healthcare organizations, CAD adoption requires integration with existing clinical workflows, validation on local patient populations, and careful consideration of liability and explainability requirements.