Integration Of Large Language Models Across The Clinical Trial Lifecycle: From Protocol Development To Regulatory Approval

Abstract

It is demonstrated here that Large Language Models (LLMs) can greatly aid with major challenges found in clinical trials. Although pharmaceutical research has advanced a lot, clinical trials continue to face problems such as low numbers of suitable patients, having too much to do with protocol development, safe monitoring and dealing with excess paperwork. It looks at the ways LLM technology helps in several sectors: assisting with finding patients through electronic record analysis, optimizing protocol setting with past data, detecting side effects and forecasting their results in real-time, handling copious regulatory documents and as well as supporting contact with different parties. Reviewing how LLMs have been implemented in various areas of medicine makes it clear that they significantly improve efficiency as well as ensuring data accuracy, protection of participants and strict protocol follow-up. Using sophisticated AI tools at every stage of clinical trials allows pharmaceutical organizations to finish their work more quickly and at lower costs, while still respecting strict rules and scientific methods which helps move vital treatments to patients faster.