Session (CTI)

Separate registration required.

This course introduces how large language models (LLMs) can enhance real-world evidence (RWE) research, highlighting practical applications and best practices. Participants will explore how LLMs streamline tasks such as literature reviews, clinical documentation analysis, patient phenotyping, and data interpretation through real-world examples.

Significant focus is given to interactive exercises that provide hands-on experience with LLM-assisted workflows, including synthesizing literature, interpreting complex data, and efficiently reviewing reports. Ethical and regulatory considerations critical to the responsible use of AI in healthcare research are also addressed, emphasizing transparency, bias mitigation, and compliance.

The course concludes by considering future AI trends, such as multimodal integration and evolving regulatory environments, preparing participants to effectively integrate these advancements into their research strategies. Participants who wish to gain hands-on experience must bring their laptops with Microsoft Excel for Windows installed.

PREREQUISITE: This course assumes that participants are familiar with the standing challenges and opportunities for RWE in research.