AI in healthcare uses machine learning to help with diagnosis, treatment planning, drug discovery, and clinical operations. From radiology models that spot tumors to ambient scribes that write clinical notes during patient visits, AI is reshaping how medicine gets practiced — but always alongside human clinicians, not replacing them.

Healthcare AI is one of the most consequential application areas for machine learning. The work spans several distinct categories. Diagnostic AI analyzes medical images — chest X-rays, mammograms, retinal scans, MRIs — and flags potential abnormalities for radiologists to review. Models from companies like Aidoc, Viz.ai, and Tempus are FDA-cleared for specific clinical use cases. Clinical documentation AI listens to doctor-patient conversations and generates structured notes, reducing the hours physicians spend on paperwork — Abridge, Nuance DAX, and Suki lead this space. Drug discovery AI predicts how molecules will behave, accelerating the search for new medicines. AlphaFold from DeepMind solved the 50-year-old protein folding problem and now lets researchers see protein structures in seconds rather than years. Operational AI handles scheduling, billing, and patient flow. Critically, healthcare AI is not autonomous — every regulatory framework requires human oversight for diagnosis and treatment decisions. The legal liability, ethical complexity, and high stakes mean AI augments clinicians rather than replacing them. The question shaping the field isn't whether AI will be involved in care delivery — it already is — but how to integrate it safely without compounding existing healthcare inequities or eroding the patient-physician relationship.

What is AI in Healthcare?