Routine Health Data Is Outperforming Trial Data for AI Training. The FDA Hasn’t Caught Up.
A study published in Nature Medicine suggests that AI models trained on routine, real-world health data outperform those trained on curated clinical trial datasets. This finding challenges current regulatory frameworks that prioritize controlled trial data for AI validation.
Three converging developments in the past eight months point to a trend that clinical operations teams have not yet named: the Routine Data Inversion . The assumption that controlled, curated clinical trial datasets produce superior AI models is quietly collapsing, and the regulatory architecture designed to validate those models was never built to handle what comes next.
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