An Agentic Generative Large Language Model for Treatment Planning of Colorectal Cancer
This study addresses the challenges of heterogeneous data integration, dynamic adherence to clinical guidelines, and safe decision-making in precision treatment for colorectal cancer by proposing GatorOnco—the first large language model that integrates agent-based reasoning with large-scale domain adaptation. The approach leverages domain-adaptive pretraining, model fusion, two-stage post-training, agent-based reinforcement learning, and retrieval-augmented generation (RAG) to enable dynamic incorporation of clinical guidelines and generate safe, controllable treatment plans. In blinded evaluations, GatorOnco significantly outperformed existing open-source large language models (P<0.01), surpassing human experts in readability and completeness while matching oncologists in correctness, timeliness, and safety.