Clinical Trials Protocol Authoring using LLMs (2404.05044v2)
Abstract: This report embarks on a mission to revolutionize clinical trial protocol development through the integration of advanced AI technologies. With a focus on leveraging the capabilities of generative AI, specifically GPT-4, this initiative aimed to streamline and enhance the efficiency and accuracy of clinical trial protocols. The methodology encompassed a detailed analysis and preparation of comprehensive drug and study level metadata, followed by the deployment of GPT-4 for automated protocol section generation. Results demonstrated a significant improvement in protocol authoring, highlighted by increases in efficiency, accuracy, and the customization of protocols to specific trial requirements. Challenges encountered during model selection and prompt engineering were systematically addressed, leading to refined methodologies that capitalized on the advanced text generation capabilities of GPT-4. This project not only showcases the practical applications and benefits of generative AI in clinical trial design but also sets a foundation for future innovations in the field.
- Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860, 2019.
- Plug and play language models: A simple approach to controlled text generation. arXiv preprint arXiv:1912.02164, 2019.
- Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
- Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858, 2019.
- The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021.
- M Maleki and M Khan. Covid-19 health equity & justice dashboard: A step towards countering health disparities among seniors and minority population. Social Science Research Network, 2023.
- Social behavior and covid-19: Analysis of the social factors behind compliance with interventions across the united states. International journal of environmental research and public health, 19(23):15716, 2022.
- Language models as knowledge bases? arXiv preprint arXiv:1909.01066, 2019.
- Mass: Masked sequence to sequence pre-training for language generation. arXiv preprint arXiv:1905.02450, 2019.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.