Narrative Review of LLMs Benchmarks for French Medical Question Answering.

Area of Science:

• Artificial Intelligence in Medicine
• Natural Language Processing
• Medical Informatics

Background:

• Generative large language models (LLMs) are increasingly used in healthcare.
• Novel LLMs for medical applications emerge daily.
• There is a need to evaluate LLMs in diverse linguistic contexts.

Purpose of the Study:

• To identify gaps in medical question-answering benchmarks.
• To focus on non-English languages, specifically French.
• To synthesize existing research on LLMs in medical French.

Main Methods:

• A narrative review methodology was employed.
• Existing research on medical LLMs and benchmarks was synthesized.
• The literature was analyzed to identify specific gaps in French medical NLP.

Main Results:

• A significant lack of a quality benchmark for medical question answering in French was identified.
• The review highlighted a gap in the development and evaluation of LLMs for French medical applications.
• Existing research predominantly focuses on English-language medical NLP.

Conclusions:

• Large language models offer significant potential for medical advancements.
• Thorough validation of LLMs in clinical practice is crucial.
• Special attention is needed for non-English medical language applications and standardized evaluations, particularly in French.