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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Large language model use in clinical oncology.

Nicolas Carl^1,2, Franziska Schramm¹, Sarah Haggenmüller¹

¹Department of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany.

NPJ Precision Oncology

|October 24, 2024

Summary

Large language models (LLMs) show varied performance in clinical oncology question answering. Standardized reporting is crucial for reliable integration of these AI tools into patient care.

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Area of Science:

Artificial Intelligence in Medicine
Oncology Research
Natural Language Processing

Background:

Large language models (LLMs) are increasingly researched for healthcare applications.
Their potential in clinical oncology requires systematic evaluation.

Purpose of the Study:

To systematically review and meta-analyze the applications, methodologies, and performance of LLMs in clinical oncology.
To identify factors contributing to performance heterogeneity and methodological disparities.

Main Methods:

A mixed-methods systematic review and meta-analysis of 34 studies.
Extraction, summarization, and comparison of LLM methodologies and outcomes in oncology.
Evaluation of LLM performance primarily on answering oncologic questions.

Main Results:

LLMs are mainly assessed for their ability to answer oncologic questions.
Significant performance variance observed, influenced by diverse methodologies and evaluation criteria.
Heterogeneity in results attributed to model capabilities, prompting strategies, and oncological subdomains.

Conclusions:

Methodological disparities exist due to a lack of standardized reporting protocols for LLMs in oncology.
Addressing these disparities is essential for comparable LLM research.
Reliable integration of LLM technologies into clinical practice necessitates standardized approaches.