Session (CTI)

PURPOSE: This panel explores the potential of Generative AI (GenAI) in addressing the unique challenges of rare diseases. The discussion focuses on three key areas: 1) Data: Unlocking data through GenAI and large language models (LLMs) to enrich research data on rare diseases. 2) Evidence: Developing rare disease-specific knowledge graphs to better represent natural history and disease pathways. 3) Insights: Generating actionable insights to address unmet patient needs and support early trial recruitment, ultimately improving patient outcomes and healthcare decision-making. DESCRIPTION: About 10% of individuals worldwide are affected by rare diseases (more than 7,000 rare diseases). Rare diseases, while individually uncommon, are collectively more prevalent than often realized. For example, over 60% of oncology drugs are approved based on specific biomarkers, redefining certain cancer subtypes as rare diseases. The evolving evidence underscores the complexity of evidence generation and decision-making for rare diseases, compounded by limited patient populations, scarce evidence, and high treatment costs. Generative AI may offer a new opportunity to address these challenges. Mr. Su (Jazz Pharma) will open the session as moderator, providing an overview of the current status of data and evidence generation for rare diseases in real-world settings. Dr. Wang (Tulane) will present recent advancements in GenAI, focusing on how LLMs and knowledge graphs can be leveraged to identify and synthesize real-world data related to rare diseases and their phenotypes from electronic health records and publicly available sources. Dr. Wang (Takeda) will discuss the application of LLMs in mining scientific literature aiming to improve early diagnosis and understanding of disease natural history in rare conditions. Finally, Dr. Wang-Silvanto (Astellas) will explore the alignment of robust data and evidence to address unmet needs, inform early trial recruitment, and support decision-making in pharmaceutical settings. The session concludes with an interactive Q&A and digital polling to encourage audience participation.