Temporal evolution of large language models (LLMs) in oncology.

ABSTRACT:

Background

Large language models (LLMs) are increasingly being applied in healthcare; however, their performance in specialized fields, such as oncology, is subject to temporal factors, including knowledge decay and concept drift. The impact of these temporal dynamics on LLM question-answering accuracy in oncology remains inadequately evaluated. This study aims to systematically assess the temporal evolution of LLM accuracy in responding to oncology-related questions using real-world data.

Method

We systematically collected relevant literature through 2025 by searching LLM-related keywords in PubMed, Google Scholar, and Web of Science databases. The inclusion criteria were as follows: (1) cancer-related research; (2) clear and complete question descriptions; and (3) complete

SUBMITTER: Qiu Z 

PROVIDER: S-EPMC12584546 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature