Guides · Specialties
AI in medicine, one specialty at a time.
AI does not arrive in medicine evenly. Radiology holds the large majority of FDA-cleared AI devices; pathology is digitizing its way toward the same curve; cardiology's clearances cluster around ECG and imaging; and at the other end, specialties like pediatrics and psychiatry face tools largely built and validated on other populations. A specialty's answer to "what should we deploy?" depends on its own evidence base, its own cleared-device list, and its own failure modes — which is why generic AI-in-healthcare overviews mislead more than they inform.
Each guide here takes one specialty and holds it to the same standard: what is actually cleared, what the peer-reviewed evidence actually shows, and what remains promise. Twelve are live — radiology, pathology, cardiology, oncology, emergency care, surgery, primary care, pediatrics, psychiatry and mental health, nursing, pharmacy, and hospital operations — each dated, tied to numbered sources, and revised as clearances and trials land. Read your own specialty first; then read a second, to see how differently the same technology behaves one department over.
12 guides in this collection
AI in cardiology: what is cleared and evidenced in 2026
Cardiology has the field's rare thing — a randomized trial where an AI alert changed diagnoses in routine care — alongside the largest consumer screening study ever run. A dated read of what is authorized and what the strongest trials measured, each figure tied to a primary source. As of July 2026.
Updated 23 Jul 2026AI in Emergency Care
A guide to artificial intelligence in the emergency department — sepsis and deterioration alerts, triage, and imaging detection for stroke and fracture — reading each result for the one thing that decides its value: whether it changed the workflow and the human response, rather than the model's accuracy alone. Each figure tied to its primary source. As of July 2026.
Updated 23 Jul 2026AI in hospital operations: a 2026 evidence guide
Beds, queues, theatres, and staff rosters are where AI in hospitals has the cleanest data and the clearest payoff — and the widest gap between prediction accuracy and proven operational benefit. What the evidence supports for patient-flow, scheduling, and command-center AI, and why an accurate forecast is only half the job. As of July 2026.
Updated 23 Jul 2026AI in nursing: a 2026 evidence guide
A strained global workforce, a documentation load measured in the hundreds of entries per shift, and the one nurse-facing AI with a randomized mortality result behind it. What the evidence supports for AI in nursing — deterioration detection, documentation, and knowledge tools — and where a nurse still has to own the output. As of July 2026.
Updated 23 Jul 2026AI in oncology: what the evidence actually shows
A guide to where artificial intelligence has earned its place across the cancer-care pathway — screening, detection, pathology — with the randomized evidence separated from the retrospective reader studies that fill vendor decks, each figure tied to its primary source. As of July 2026.
Updated 23 Jul 2026AI in pathology: what is cleared and evidenced in 2026
Pathology has the field's most striking research results and one of its smallest cleared footprints. A dated read of what the FDA has authorized in digital pathology and what the strongest studies actually measured, each figure tied to a primary source. As of July 2026.
Updated 23 Jul 2026AI in pediatrics: a 2026 guide
Pediatrics is the specialty where clinical AI is furthest behind — for structural reasons — yet it holds some of the field's most striking proofs of concept. This guide pairs the landmark evidence with the data and device gaps that explain why so little is cleared for children. As of July 2026.
Updated 23 Jul 2026AI in pharmacy: a 2026 evidence guide
What the published record actually supports for AI in pharmacy — where dispensing automation has measured safety gains, why interruptive drug-interaction alerts are overridden roughly nine times in ten, and what a language model can and cannot be trusted to do with a drug question. As of July 2026.
Updated 23 Jul 2026AI in primary care: exam scores versus patient outcomes
A guide to artificial intelligence at the point of first contact — decision support, conversational diagnosis, autonomous screening, and ambient documentation — reading the field's benchmark hype against the handful of large trials that measured what happened to patients. Each figure tied to its primary source. As of July 2026.
Updated 23 Jul 2026AI in psychiatry and mental-health chatbots: a 2026 guide
Mental-health chatbots are the most consumer-facing and most contested use of AI in healthcare. This guide separates three things the market blurs — structured tools with trial evidence, general-purpose chatbots with documented safety failures, and the fast-moving 2025 regulation — with primary sources throughout. As of July 2026.
Updated 23 Jul 2026AI in radiology: what is cleared and evidenced in 2026
Radiology is where clinical AI is most deployed and most authorized — but the cleared reality and the trial evidence are two different maps. A dated read of the FDA device record and the strongest randomized trial in imaging, each figure tied to a primary source. As of July 2026.
Updated 23 Jul 2026AI in surgery: a 2026 guide
What artificial intelligence actually does in the operating room today — from polyp detection and anatomy guidance to surgical-phase recognition and the first supervised-autonomy demonstrations — with every capability tied to its primary evidence and its regulatory status. As of July 2026.