  The intricate relationship between genetic variation and human diseases has
been a focal point of medical research, evidenced by the identification of risk
genes regarding specific diseases. The advent of advanced genome sequencing
techniques has significantly improved the efficiency and cost-effectiveness of
detecting these genetic markers, playing a crucial role in disease diagnosis
and forming the basis for clinical decision-making and early risk assessment.
To overcome the limitations of existing databases that record disease-gene
associations from existing literature, which often lack real-time updates, we
propose a novel framework employing Large Language Models (LLMs) for the
discovery of diseases associated with specific genes. This framework aims to
automate the labor-intensive process of sifting through medical literature for
evidence linking genetic variations to diseases, thereby enhancing the
efficiency of disease identification. Our approach involves using LLMs to
conduct literature searches, summarize relevant findings, and pinpoint diseases
related to specific genes. This paper details the development and application
of our LLM-powered framework, demonstrating its potential in streamlining the
complex process of literature retrieval and summarization to identify diseases
associated with specific genetic variations.
