Benchmarking the diagnostic performance of open source LLMs in 1933 Eurorad case reports.

ABSTRACT: Recent advancements in large language models (LLMs) have created new ways to support radiological diagnostics. While both open-source and proprietary LLMs can address privacy concerns through local or cloud deployment, open-source models provide advantages in continuity of access, and potentially lower costs. This study evaluated the diagnostic performance of fifteen open-source LLMs and one closed-source LLM (GPT-4o) in 1,933 cases from the Eurorad library. LLMs provided differential diagnoses based on clinical history and imaging findings. Responses were considered correct if the true diagnosis appeared in the top three suggestions. Models were further tested on 60 non-public brain MRI cases from a tertiary hospital to assess generalizability. In both datasets, GPT-4o demonstrated superi

SUBMITTER: Kim SH 

PROVIDER: S-EPMC11814077 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature