Transforming clinical trials: the emerging roles of large language models.

English summary

This review examines the potential for large language models (LLMs) to address challenges in patient–trial matching and clinical trial planning and to transform trial operations.

Key points

• Five near-term application areas are identified: improving patient–trial matching, streamlining trial planning, coding and classifying free-text clinical narratives, assisting with technical writing, and providing cognizant consent through LLM-powered chatbots.
• LLMs may improve clinical-trial efficiency, support innovative methodologies, and enhance patient engagement.
• Important challenges include validating accuracy and addressing legal concerns. Careful consideration and investment are needed to overcome potential barriers to implementation.

Audience

Clinical-trial researchers, clinical pharmacologists, medical-AI and data-science researchers, and advanced students

Scope and limitations

This summary is based on the supplied review-level source text. The source does not provide detailed methods, performance metrics, implementation examples, validation procedures, or specific legal analyses.

Research context

The topic aligns thematically with medical AI and data science through its focus on applying LLMs to clinical-trial design, operations, and unstructured clinical information.