AI Enters the Clinical Mainstream
Artificial intelligence is no longer a future promise in medicine — it is diagnosing disease today. Across radiology, pathology, dermatology, and cardiology, FDA-cleared AI algorithms are reading scans, analyzing biopsies, and detecting patterns invisible to the human eye. The question is no longer whether AI can contribute to medicine, but how to integrate it wisely into clinical workflows.
Deep learning systems trained on millions of images can detect diabetic retinopathy in fundus photographs with accuracy matching ophthalmologists. Google’s DeepMind developed an AI system for detecting over 50 eye conditions from optical coherence tomography scans, recommending correct referrals 94% of the time. These tools are expanding access to specialist-level diagnosis in regions with few specialists.
The path from promising research to regulatory approval has accelerated. The FDA cleared its first AI-based medical device in 1995, but only approved 100 such algorithms in total by 2020. By 2025, that number exceeded 700, with radiology and cardiovascular imaging leading the way. The regulatory machinery is adapting to handle adaptive AI systems that continue learning after deployment.
