Unlocking the Potential of Generative AI for Clinical Trial Matching: Opportunities and Equity Considerations

Music City Center 

The integration of generative artificial intelligence (AI), particularly large language models (LLMs), presents transformative opportunities to enhance clinical trial matching—a traditionally labor-intensive process limiting patient access to potential therapies. Recent advancements demonstrate that both proprietary models (e.g., GPT-3.5, GPT-4) and fine-tuned open-source LLMs (e.g., LLAMA-based models like Trial-LLAMA and OncoLLM) can effectively interpret complex eligibility criteria and match patients using real-world electronic health records (EHRs), achieving performance comparable to medical professionals. These developments promise increased efficiency, reduced costs, and improved privacy and reproducibility in patient-trial matching.

However, the adoption of LLMs in healthcare raises critical equity considerations. Studies reveal that LLMs could potentially exacerbate existing health disparities for clinical trial matching. Furthermore, establishing comprehensive ethical principles is essential for the responsible deployment of generative AI in healthcare, and robust human evaluation methodologies of LLM applications in healthcare are crucial for ensuring safety and effectiveness.

In this talk, we will share recent clinical trial matching system empowered by LLMs developed by our team in collaboration with industry partners, and discuss our recent research on the "GREAT PLEA" ethical principles, the EquityGuard framework, and the QUEST human evaluation framework for LLM applications in healthcare.

Generative AI

Clinical Trial Matching

Large Language Models

Health Equity