The use of large language models to enhance cancer clinical trial educational materials
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929614579826688 |
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| author | 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 Bitterman, Danielle S. |
| author_facet | 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 Bitterman, Danielle S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01955 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The use of large language models to enhance cancer clinical trial educational materials 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 Bitterman, Danielle S. Computation and Language Artificial Intelligence 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. |
| title | The use of large language models to enhance cancer clinical trial educational materials |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.01955 |