A platform for the biomedical application of large language models

Abstract

Generative artificial intelligence (AI) has advanced considerably in recent years, particularly in the domain of language. However, despite its rapid commodification, its use in biomedical research is still in its infancy^1,2. The two main avenues for using large language models (LLMs) are end-user-ready platforms, which are usually provided by large corporations, and custom solutions developed by individual researchers with programming knowledge. Both use cases have significant limitations. Commercial platforms do not meet the transparency standards required for reproducible research; none are open source, and only a few provide (superficial) scientific descriptions of their algorithms³. They are also subject to privacy concerns (reuse of user data) and to considerable commercial pressures. In addition, they are not fully customizable to accommodate a specific research domain or workflow.

Individual solutions, on the other hand, are not accessible to most biomedical researchers. They require many specialized skills in addition to the researcher’s domain-specific knowledge, such as programming, data management, machine learning knowledge, technical expertise in deployment and frameworking, and management of software versions in a rapidly changing environment. This, in turn, prevents robust and reproducible results owing to the many technical challenges involved. As a result, applications of LLMs in biomedical research are still at the level of individual case studies^2,4, in contrast to the imaging domain, which boasts several open-source AI frameworks and approved medical devices¹.