Large language models for automating clinical trial matching

Abstract

PURPOSE OF REVIEW: The uses of generative artificial intelligence (GAI) technologies in medicine are expanding, with the use of large language models (LLMs) for matching patients to clinical trials of particular interest. This review provides an overview of the current ability of leveraging LLMs for clinical trial matching. RECENT FINDINGS: This review article examines recent studies assessing the performance of LLMs in oncologic clinical trial matching. The research in this area has shown promising results when testing these system using artificially created datasets. In general, they looked at how LLMs can be used to match patient health records with clinical trial eligibility criteria. There is still a need for human oversight of the systems in their current state. SUMMARY: Automated clinical trial matching can improve patient access and autonomy, reduce provider workload, and increase trial enrollment. However, it may potentially create a feeling of "false hope" for patients, can be difficult to navigate, and still requires human oversight. Providers may face a learning curve, while institutions must address data privacy concerns and ensure seamless EMR/EHR integration. Given this, additional studies are needed to ensure safety and efficacy of LLM-based clinical trial matching in oncology.

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