Clinical agents lift heart-failure AUROC to 0.963

Nimblemind’s multi-agent pipeline raised held-out heart-failure phenotyping AUROC from 0.895 to 0.963 after generating 132 structured and 70 rubric-scored features from 500 patient records, according to the evidence-linked EHR study. The paper targets feature engineering that consumes 39% to 45% of clinical data scientists’ workload, but its single-institution cohort leaves external validity unresolved. Builders following the economics of AI drug discovery should file the gain as a pilot signal, not a deployment threshold, and preserve provenance for every generated feature.