Cohort‑aware agents for individualized lung cancer risk prediction using a retrieval‑augmented model selection framework

, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TD-VIT, DLSTM), and multi-modal computer vision based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline-retrieval via FAISS and reasoning via LLM-enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.