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Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Topics and Characteristics of Registered Studies on LLMs
Christian Thiele1, Gerrit Hirschfeld1
1Bielefeld University of Applied Sciences.
Abstract:
Large Language Models (LLMs) are promoted as solutions to many problems in medicine and wider health care. However, the empirical evidence of these claims is currently limited, as clinical trials usually take several years until publication. Clinical trial registries, such as ClinicalTrials.gov, allow for a glimpse into the topics on which publications can be expected in the future. The aim of the present study is to identify studies on ClinicalTrials.gov that use LLMs and to summarize their characteristics and topics. We identified 94 studies involving LLMs after keyword-based screening and subsequent manual inspection. All studies had start dates in 2023 or later. Compared to other studies, LLM-studies relatively often had the primary purpose "health services research", while "treatment" was relatively rare. The most common topics of LLM-studies were diagnostics, clinical recommendations, and other supportive functions. These findings underscore that LLMs are currently not being evaluated for treatment, prevention, or drug discovery, but rather for their linguistic and reasoning capabilities as assistive tools.
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