Streamlining Clinical Trial Recruitment with LLMs: Using Gen AI to match eligible patients with ongoing clinical trials by analyzing medical records, trial requirements, and patient preferences

Antony Ronald Reagan P

doi:10.36948/ijfmr.2022.v04i05.39037

Streamlining Clinical Trial Recruitment with LLMs: Using Gen AI to match eligible patients with ongoing clinical trials by analyzing medical records, trial requirements, and patient preferences

Author(s)	Antony Ronald Reagan P
Country	United States
Abstract	Recruitment of patients as clinical trials participants is still one of the major and most significant barriers to successful medical study. Challenges experienced in individual participant consent, trial allocation, and addressing issues of practicality and compliance give rise to program delays and results in higher expenditures. The operational utility of Generative AI, especially LLMs in this regard, will dramatically change this process by quickly sifting through the mountains of patient data and medical records or the trial specifications to come up with suitable candidates for clinical trials. The second major category of cognitives, the LLMs, employ NLP and are designed for understanding medical jargons, patents histories and trial requirements, which makes them perfect for the task of recruiting. Using EHRs, medical histories, and patients' preferences, LLMs can identify the patient for current active trials relevant to their medical conditions and treatment. Moreover, the possibility of using LLMs in filtering and analyzing data in real-time makes a huge difference in efficiency as well as lowers recruitment costs and time for researchers and clinicians. Such steps make a trial more probable to accrue a diverse set of patients while at the same time make sure that trial is done within the required time. Further, LLMs allow for individualization in clinical trial participation by taking into account patient's concerns which may include geographic site preference, the ability to adhere to trial requirements, and treatment-expectancy. When patient data is matched with trial characteristics, LLMs increase both quality and speed of recruitment and, subsequently, the number of patients qualified for trials and, presumably, a decreased number of trial stoppages for lack of enrollment.
Field	Engineering
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-10-04
DOI	https://doi.org/10.36948/ijfmr.2022.v04i05.39037
Short DOI	https://doi.org/g88g9q

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Streamlining Clinical Trial Recruitment with LLMs: Using Gen AI to match eligible patients with ongoing clinical trials by analyzing medical records, trial requirements, and patient preferences

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