Large language models could transform clinical trials: New review highlights opportunities and challenges

Clinical trials are essential for advancing medical innovation, but they face growing challenges — from recruiting eligible participants to managing complex data. In a new review published in BMC Medicine, researchers detail how large language models (LLMs), a type of advanced artificial intelligence trained on vast amounts of text, could help streamline these processes.

By automatically extracting research elements from prior studies, refining eligibility criteria, and tailoring informed consent materials, LLMs could improve trial design quality and participant understanding. In trial operations, they show potential for faster, more accurate patient screening, standardized data collection, and real-time safety monitoring, including adverse event and drug–drug interaction detection. LLMs may also predict trial outcomes or simulate trial scenarios, enabling more informed decision-making.

The review notes that LLMs outperform traditional natural language processing models in context comprehension, adaptability, and multitask execution. However, risks remain, including the possibility of generating inaccurate information, sensitivity to prompt wording, and the inability to update knowledge without retraining. The authors emphasize that integrating LLMs into clinical trials will require strict data privacy safeguards, transparent model evaluation, and clear regulatory guidelines.