Clinical reasoning from real-world oncology reports using large language models.

ABSTRACT:

Objective

To evaluate the ability of large language models (LLMs) to perform structured information extraction and guideline-based clinical inferences from radiology and pathology reports in real-world oncology.

Methods

We constructed a Question Answering (Q&A) benchmark dataset using 3650 radiological and 588 pathological reports from 1632 patients. The tasks included direct extraction of genomic and histological findings, as well as clinical reasoning tasks, such as Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response classification and American Joint Committee on Cancer (AJCC)-based tumor-node-metastasis (TNM) staging. We compared the performance of the Gemma family of open-source LLMs (Gemma 4B, a lightweight 4-billion parameter model, and Gemma 12B

SUBMITTER: Park JH 

PROVIDER: S-EPMC12615922 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature