Four Clinical AI Diagnostic Tasks and How To Evaluate Them
Triage, detection, characterization, and autonomous diagnosis make different clinical claims. Here is the evidence question that belongs to each one.
AI for retinal image analysis, diabetic retinopathy screening, and eye disease detection.
Ophthalmology has produced some of the earliest FDA-cleared AI medical devices. AI systems can analyze fundus photographs to detect diabetic retinopathy, glaucoma, and age-related macular degeneration with high accuracy.
Ophthalmology holds a position no other specialty does: it is where autonomous AI became legal in American medicine.
The first autonomous diagnostic device authorized in the United States screens retinal photographs for diabetic retinopathy and returns a result with no clinician viewing the image. Every other device on the FDA list produces something a clinician interprets. This category produces a determination.
Retinal photography is cheap, standardized, and repeatable. Diabetic retinopathy is common, has a clear grading standard, and has a screening question with a binary answer. And the clinical gap is enormous: a large share of diabetic patients never reach an eye clinic at all, so the comparator for autonomous screening is frequently no screening rather than specialist screening.
That last point is what made the risk calculation work. A device does not have to match an ophthalmologist to improve outcomes in a population that was not seeing one.
This is the best-evidenced category in medical AI, with prospective validation at primary care sites using the intended non-specialist staff. The caveats are operational rather than technical: ungradable image rates are real, screening is indicated for one disease and does not assess other retinal pathology, and the benefit evaporates entirely if positives do not actually reach an ophthalmologist. Referral leakage is the recurring failure in real programs.
3 records currently tracked.
Triage, detection, characterization, and autonomous diagnosis make different clinical claims. Here is the evidence question that belongs to each one.
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