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Evaluating Medium Scale, Open-Source Large Language Models: Towards Decision Support in a Precision Oncology Care

Kevin Kaufmes¹, Georg Mathes¹, Dilyana Vladimirova²

¹MOLIT Institute, Heilbronn, Germany.

Studies in Health Technology and Informatics

|September 3, 2025

Summary

Medium-scale large language models (LLMs) show insufficient reliability for precision oncology molecular tumor board (MTB) preparation. Current LLMs frequently provide outdated or incorrect information, posing risks to patient safety.

Keywords:

Benchmarking Clinical Decision Support Systems Large Language Models Medical Oncology Precision Medicine

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Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems

Background:

Precision oncology requires up-to-date knowledge for complex patient cases.
Preparing cases for molecular tumor boards (MTBs) is labor-intensive.
Large language models (LLMs) offer potential to streamline information retrieval for MTBs.

Purpose of the Study:

To evaluate the utility of medium-scale LLMs for answering clinical questions in MTB preparation.
To assess the performance of on-premise LLMs using consumer hardware for sensitive data handling.

Main Methods:

Three LLMs were selected based on benchmarks and reasoning capabilities.
Domain experts provided exemplary MTB-related questions.
Experts evaluated LLM-generated responses for quality and correctness.

Main Results:

Overall LLM performance was modest, with significant issues identified.
A high percentage of responses contained outdated, incomplete, or factually erroneous information.
Evaluator discordance and varying confidence levels were observed.

Conclusions:

Medium-scale LLMs are currently unreliable for precision oncology applications.
Outdated information and confident misinformation highlight a gap between benchmark and real-world performance.
Future research should explore advanced techniques like RAG and web search, prioritizing patient safety.