Moffitt Cancer Center Uses Multi-Agent AI to Extract Social Risk Data From Clinical Notes

At the AACR Annual Meeting 2026, researchers from Moffitt Cancer Center presented a multi-agent artificial intelligence system designed to extract social determinants of health (SDOH) from unstructured clinical records. The system, developed within Moffitt’s machine learning division, uses coordinated large language model agents to identify non-clinical factors that influence cancer outcomes.

Ghulam Rasool, Associate Member in the Department of Machine Learning, presented the work at the conference. He said patient survival is not determined by tumor biology alone but also by factors such as financial hardship, lack of transportation, and unstable housing. These signals are typically embedded in free-text clinical documentation and are not captured in structured fields within electronic health records.

Rasool said the team initially tested a single large language model but found limitations in consistency and traceability. The system was then redesigned as a multi-agent architecture consisting of six AI agents that collaborate to answer queries. Each agent contributes to interpreting the question, retrieving relevant evidence from patient notes, and validating the output. The system also provides provenance, identifying the exact location in the clinical record where the information was found.

The model was validated on multiple datasets, including a pancreatic cancer cohort. Rasool said the system achieved more than 98% accuracy in extracting SDOH-related information when it was present in the documentation. He said this means the system can reliably retrieve instances where patients experienced issues related to housing, childcare, or financial constraints, as long as those details were recorded in clinical notes.