Presentation (CTI)

OBJECTIVES: Over 7,000 known rare diseases affect ~300 million patients worldwide. However, qualified Key Opinion Leaders (KOLs) with expertise in rare conditions are often difficult to identify using traditional methods, presenting challenges in identification and limiting collaborative evidence generation opportunities. This study evaluates trends in rare disease KOL identification to benchmark traditional methods against AI-driven approaches, highlighting opportunities to improve research collaboration.
METHODS: Primary research was conducted with industry leaders to identify current tactics, tools, and gaps in KOL identification methods. Building on these insights, we aim to evaluate various AI-enabled tools and unconventional resources (e.g., patient advocacy networks) for their effectiveness, scalability, and potential to address existing gaps.
RESULTS:

• Challenges Identified: A limited pool of rare disease KOLs leads to over-reliance on a few prominent experts, creating biases in research priorities and decision-making.
• Trends in KOL Identification: Increasing use of AI and data analytics enable broader and deeper insights into KOL landscapes.
• Benchmarking Outcomes: AI-enhanced tools demonstrate advantages in identifying underrecognized experts, uncovering networks, and decrease duration of workstreams by ~50%.

Forward-looking Opportunities:

1. AI Integration: Employ machine learning (ML) and natural language processing (NLP) to aggregate and analyze data from diverse sources (e.g., gray literature, clinical trial databases, social media).
2. Unconventional Data Mining: Expand searches to include patient advocacy groups, community networks, and non-traditional publication platforms.
3. Collaborative Databases: Develop centralized, AI-enhanced registries of rare disease experts, fostering equitable access for researchers, policymakers, and industry stakeholders.

CONCLUSIONS: Partnering with KOLs adds rigor and credibility to evidence generation workstreams, including HEOR and Medial Affairs; leveraging AI tools offers a new opportunity to identify KOLs. Future research should focus on validating AI-driven methodologies in real-world settings, assessing their impact on rare disease research and policy outcomes, and addressing ethical considerations to ensure transparency and inclusivity.