Weill Cornell Medicine researchers have designed an AI system that could help clinical laboratories identify problems before they disrupt testing and delay patient care. These laboratories analyze samples such as blood, urine, tissue or other bodily fluids to help doctors diagnose and treat diseases. However, growing test volumes, workforce shortages and increasingly complex diagnostics are placing mounting demands on laboratory operations. In a perspective published Oct. 28 in npj Health Systems, the researchers describe Laboratory Agentic Reasoning and Integrative Analytics (LabARIA), one of the first frameworks using several specialized AI tools called agents to help monitor laboratory quality and operations more efficiently. Dr. Fei Wang “Laboratory medicine is a critical but largely neglected area, especially considering the AI work already done in radiology and pathology,” said senior author Dr. Fei Wang, associate dean for artificial intelligence and the Frances and John Loeb Professor of Medical Informatics in the Department of Population Health Sciences at Weill Cornell. Existing AI algorithms developed for laboratory medicine are fragmented and largely confined to task-specific silos, the authors write. LabARIA is designed to integrate these capabilities, allowing the system to identify patterns across different aspects of laboratory operations that individual tools might miss. The proposed framework would connect to existing laboratory information systems and other databases rather than replace them. A central AI “data coordinator” would route information to specialized AI agents that monitor specimen quality, testing turnaround times, analytical quality control, instrument performance and regulatory compliance. Other agents would forecast inventory and staffing needs. Dr. He Sarina Yang “This framework can identify anomalies and pull data together to help lab personnel investigate and address problems earlier,” said lead author Dr. He Sarina Yang,…

Weill Cornell Medicine and NewYork-Presbyterian investigators have built a machine-learning tool that can predict SARS-CoV-2 infection based on age, gender, race and 27 routine laboratory test results.