Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data.

ABSTRACT:

Background

Traditional cancer registries, limited by labor-intensive manual data abstraction and rigid, predefined schemas, often hinder timely and comprehensive oncology research. While large language models (LLMs) have shown promise in automating data extraction, their potential to perform direct, just-in-time (JIT) analysis on unstructured clinical narratives-potentially bypassing intermediate structured databases for many analytical tasks-remains largely unexplored.

Objective

This study aimed to evaluate whether a state-of-the-art LLM (Gemini 2.5 Pro) can enable a JIT clinical oncology analysis paradigm by assessing its ability to (1) perform high-fidelity multiparameter data extraction, (2) answer complex clinical queries directly from raw text, (3) automate multistep s

SUBMITTER: May P 

PROVIDER: S-EPMC12670046 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature