Cornerstone AI finds 20% more lab records

Cornerstone AI says its study of roughly 4 million laboratory records from 19,415 patients reached 99.9% frequency-weighted LOINC-component accuracy, versus 93.5% for manual workflows, and surfaced 20% more MASH-related records. The study announcement says AI found 801,874 relevant records against 667,857 manually identified, with results due for presentation at the ISPE annual meeting. The builder’s takeaway is a useful evaluation target for scientific data pipelines—measure completeness as well as mapping accuracy, then demand independent adjudication before trusting the automated research workflow.