Paper
RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale
Authors
Jason Holmes, Yuexing Hao, Mariana Borras-Osorio, Federico Mastroleo, Santiago Romero Brufau, Valentina Carducci, Katie M Van Abel, David M Routman, Andrew Y. K. Foong, Liv M Muller, Satomi Shiraishi, Daniel K Ebner, Daniel J Ma, Sameer R Keole, Samir H Patel, Mirek Fatyga, Martin Bues, Brad J Stish, Yolanda I Garces, Michelle A Neben Wittich, Robert L Foote, Sujay A Vora, Nadia N Laack, Mark R Waddle, Wei Liu
Abstract
Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based agent capable of independently retrieving patient-specific information, iteratively assessing evidence, and returning structured outcomes. Our evaluation explicitly validates RadOnc-GPT across two clearly defined tiers of increasing complexity: (1) a structured quality assurance (QA) tier, assessing the accurate retrieval of demographic and radiotherapy treatment plan details, followed by (2) a complex clinical outcomes labeling tier involving determination of mandibular osteoradionecrosis (ORN) in head-and-neck cancer patients and detection of cancer recurrence in independent prostate and head-and-neck cancer cohorts requiring combined interpretation of structured and unstructured patient data. The QA tier establishes foundational trust in structured-data retrieval, a critical prerequisite for successful complex clinical outcome labeling.