Editing Ai Healthcare (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Healthcare AI evaluation has stricter requirements than most AI domains:

'''Clinical study design''': Retrospective studies (model trained and tested on historical data) establish feasibility; prospective studies (model deployed in real clinical workflow) establish real-world utility. Only randomized controlled trials (RCTs) comparing outcomes with vs. without AI establish clinical effectiveness. Very few AI systems have RCT evidence; this is a critical gap.

'''Subgroup analysis by clinically relevant factors''': Age, sex, race/ethnicity, disease severity, imaging equipment manufacturer, institution. Performance disparities across subgroups must be explicitly reported.

'''Operating point selection''': A classifier's threshold must be calibrated for the clinical context. Screening (e.g., diabetic retinopathy) demands high sensitivity (catch all cases) even at cost of lower specificity (more false positives for follow-up). Diagnosis may prioritize specificity.

'''Clinical utility vs. model accuracy''': The key question is not "is the AI accurate?" but "does the AI improve patient outcomes when integrated into the clinical workflow?" An AI that increases radiologist efficiency by 30% without missing cases has proven clinical utility regardless of how it compares to radiologist-alone accuracy.

Expert practitioners work with clinical experts from the beginning — not just as annotators, but as partners in defining the clinical question, the evaluation criteria, and the deployment workflow.
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