Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches
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Summary
This summary is machine-generated.Large language models (LLMs) are revolutionizing clinical trial recruitment by improving patient-trial matching. These advanced AI systems enhance accuracy and scalability, addressing key bottlenecks in trial execution.
Area Of Science
- Artificial Intelligence in Clinical Research
- Biomedical Informatics
- Clinical Trial Operations
Background
- Patient recruitment is a significant challenge in clinical trials, often leading to delays and failures.
- Inefficient patient-trial matching contributes to these recruitment difficulties.
- Large language models (LLMs) offer potential solutions for automating and optimizing patient-trial matching.
Purpose Of The Study
- To conduct a scoping review of emerging applications of LLMs in patient-trial matching.
- To synthesize current research on LLM integration for improving clinical trial recruitment.
Main Methods
- A comprehensive literature search was performed in PubMed, Web of Science, and OpenAlex (December 2022 - December 2024).
- Studies integrating LLMs into patient-trial matching systems were included.
- Data extraction focused on system architecture, data processing, eligibility criteria, matching techniques, and performance evaluation.
Main Results
- 24 studies met the inclusion criteria, with a surge in publications in 2024.
- Most systems utilized patient-centric matching and generative pretrained transformer models.
- LLM approaches showed improved accuracy and scalability but faced challenges in performance variability, interpretability, and data dependency.
Conclusions
- LLM-based systems offer a transformative approach to enhance clinical trial recruitment efficiency and accuracy.
- Addressing limitations in generalizability, explainability, and data constraints is crucial for future optimization.
- Advancements in hybrid modeling, domain-specific fine-tuning, and real-world data integration will further streamline trial matching and execution.
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Related Concept Videos
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.