1. The use of large language models to enhance cancer clinical trial educational materials
- Author
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Gao, Mingye, Varshney, Aman, Chen, Shan, Goddla, Vikram, Gallifant, Jack, Doyle, Patrick, Novack, Claire, Dillon-Martin, Maeve, Perkins, Teresia, Correia, Xinrong, Duhaime, Erik, Isenstein, Howard, Sharon, Elad, Lehmann, Lisa Soleymani, Kozono, David, Anthony, Brian, Dligach, Dmitriy, and Bitterman, Danielle S.
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in generating patient-friendly educational content from clinical trial informed consent forms. Using data from ClinicalTrials.gov, we employed zero-shot learning for creating trial summaries and one-shot learning for developing multiple-choice questions, evaluating their effectiveness through patient surveys and crowdsourced annotation. Results showed that GPT4-generated summaries were both readable and comprehensive, and may improve patients' understanding and interest in clinical trials. The multiple-choice questions demonstrated high accuracy and agreement with crowdsourced annotators. For both resource types, hallucinations were identified that require ongoing human oversight. The findings demonstrate the potential of LLMs "out-of-the-box" to support the generation of clinical trial education materials with minimal trial-specific engineering, but implementation with a human-in-the-loop is still needed to avoid misinformation risks.
- Published
- 2024