GHGA Lecture Series: Advances in Data-Driven Biomedicine (Tudor Groza)

Most Rare Diseases lack established treatment protocols, highlighting the need for proper care pathways addressing prognosis, diagnosis, and management. Advances in Generative AI and Large Language Models (LLMs) offer new opportunities to document the temporal progression of phenotypic features, addressing gaps in current knowledge bases. This talk introduces an LLM-based framework to capture the natural history of diseases, including a specific use case that focuses on Noonan syndrome.

Tudor Groza from the Agency for Science, Technology and Research (A*STAR, Singapore) will present how advances in Generative AI and Large Language Models (LLMs) offer new opportunities to document the temporal progression of phenotypic features, addressing gaps in prognosis, diagnosis, and patient management. He will introduce an LLM-based framework to capture the natural history of diseases, including a specific use case that focuses on Noonan syndrome. 

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