  The wealth of knowledge we have amassed in the context of biomedical science
has grown exponentially in the last decades. Consequently, understanding and
contextualising scientific results has become increasingly difficult for any
single individual. In contrast, current Large Language Models (LLMs) can
remember an enormous amount of information, but have notable shortcomings, such
as a lack of generalised awareness, logical deficits, and a propensity to
hallucinate. To improve biomedical analyses, we propose to combine human
ingenuity and machine memory by means of an open and modular conversational
platform, biochatter (https://github.com/biocypher/biochatter), exemplified in
the web application ChatGSE (https://chat.biocypher.org). We safeguard against
common LLM shortcomings using general and biomedicine-specific measures and
allow automated integration of popular bioinformatics methods. Ultimately, we
aim to improve the AI-readiness of biomedicine and make LLMs more useful and
trustworthy in research applications.

Quantitative Methods