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

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 survival analyses including executable code generation, and (4) generate novel, clinically plausible hypotheses from free-text documentation. Methods: A synthetic dataset of 240 unstructured clinical letters from patients with stage IV non-small cell lung cancer (NSCLC), embedding 14 predefined variables, was used. Gemini 2.5 Pro was evaluated on four core JIT capabilities. Performance was measured by using the following metrics: extraction accuracy (compared to human extraction of n=40 letters and across the full n=240 dataset); numerical deviation for direct question answering (n=40 to 240 letters, 5 questions); log-rank P value and Harrell concordance index for LLM-generated versus ground-truth Kaplan-Meier survival analyses (n=160 letters, overall survival and progression-free survival); and correct justification, novelty, and a qualitative evaluation of LLM-generated hypotheses (n=80 and n=160 letters). Results: For multiparameter extraction from 40 letters, the LLM achieved >99% average accuracy, comparable to human extraction, but in significantly less time (LLM: 3.7 min vs human: 133.8 min). Across the full 240-letter dataset, LLM multiparameter extraction maintained >98% accuracy for most variables. The LLM answered multiconditional clinical queries directly from raw text with a relative deviation rarely exceeding 1.5%, even with up to 240 letters. Crucially, it autonomously performed end-to-end survival analysis, generating text-to-R-code that produced Kaplan-Meier curves statistically indistinguishable from ground truth. Consistent performance was demonstrated on a small validation cohort of 80 synthetic acute myeloid leukemia reports. Stress testing on data with simulated imperfections revealed a key role of a human-in-the-loop to resolve AI-flagged ambiguities. Furthermore, the LLM generated several correctly justified, biologically plausible, and potentially novel hypotheses from datasets up to 80 letters. Conclusions: This feasibility study demonstrated that a frontier LLM (Gemini 2.5 Pro) can successfully perform high-fidelity data extraction, multiconditional querying, and automated survival analysis directly from unstructured text. These results provide a foundational proof of concept for the JIT clinical analysis approach. However, these findings are confined to synthetic patients, and rigorous validation on real-world clinical data is an essential next step before clinical implementation can be considered.

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