Summary of The Use Of Large Language Models to Enhance Cancer Clinical Trial Educational Materials, by Mingye Gao et al.
The use of large language models to enhance cancer clinical trial educational materials
by Mingye Gao, Aman Varshney, Shan Chen, Vikram Goddla, Jack Gallifant, Patrick Doyle, Claire Novack, Maeve Dillon-Martin, Teresia Perkins, Xinrong Correia, Erik Duhaime, Howard Isenstein, Elad Sharon, Lisa Soleymani Lehmann, David Kozono, Brian Anthony, Dmitriy Dligach, Danielle S. Bitterman
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study explores the potential of Large Language Models (LLMs), particularly GPT4, in generating patient-friendly educational content from clinical trial informed consent forms. The researchers 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 show that GPT4-generated summaries were readable and comprehensive, potentially improving patients’ understanding and interest in clinical trials. Multiple-choice questions demonstrated high accuracy and agreement with crowdsourced annotators. However, hallucinations were identified that require ongoing human oversight. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using big computer models (LLMs) to help people understand clinical trials better. The researchers used these models to create summaries of trial information and questions for patients. They tested these materials with patients and other people who looked at them online. The results show that the model-created summaries were easy to read and understood, which could make patients more interested in participating in trials. The questions also worked well. But there’s a problem – sometimes the models made things up, so humans need to check their work. |
Keywords
» Artificial intelligence » One shot » Zero shot
