Clinical Prediction Models Are Breaking Trials and Nobody Is Requiring a Fix

A Dana Farber-linked research team recently ran an AI-assisted scoping review of roughly 4,000 published clinical prediction model studies and found that code sharing rates remained low, with recent years showing modest improvement. The direction is right. The magnitude is damning. When sponsors and investigators build prediction models that guide patient selection, endpoint stratification, or risk scoring in a trial, and then publish those models without sharing the underlying code, the work cannot be verified, the results cannot be reproduced, and the regulatory record is silent on the gap.

Three signals have emerged in the past year that, taken individually, look like routine methodological criticism. Taken together, they describe something the clinical operations community has not yet named: a reproducibility debt embedded directly in AI-driven trial infrastructure. The debt is accumulating faster than any existing regulatory mechanism can collect it.