Creating a Secure and Transparent Framework for LLM-Enhanced Clinical Trials and Predictive Models: A Technical Report – Cureus

The technical report “Building a Safe and Transparent Workflow for Large Language Model (LLM)-Assisted Clinical Trials and Prediction Models” emphasizes the integration of LLMs in clinical research. It outlines a framework to ensure the safety, transparency, and reproducibility of LLM applications in healthcare. Key components include establishing clear protocols, data management practices, and ethical guidelines for utilizing LLMs in clinical trials. The report advocates for robust validation mechanisms to enhance trustworthiness and mitigate potential biases inherent in language models. Additionally, it highlights the importance of interdisciplinary collaboration among clinicians, data scientists, and ethicists to create a cohesive workflow. By prioritizing patient safety and regulatory compliance, this framework aims to pave the way for the responsible use of AI in medical research, ultimately improving prediction accuracy and patient outcomes. Adopting these best practices will facilitate a safer and more effective integration of LLMs in the clinical research landscape.

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