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33% of clinical trials have problems with randomization, statistical analysis and patient recruitment. AI assists in several bottlenecks.
A clinical study is a scientific investigation designed to evaluate the safety and efficacy of a medical treatment or intervention in humans. These studies may involve patients, healthy volunteers, or both, and aim to determine if a medical intervention is safe, what side effects it may have, and its effectiveness in treating a particular disease or condition (1). Clinical studies can have various designs, including randomized and controlled studies, and are categorized into phase I, II, III, or IV based on their objectives and developmental stages (2)(3). However, challenges persist; a study from the University of Toronto found that about 33% of randomized clinical trials published in major medical journals had issues with randomization, blinding, or statistical analysis, impacting the validity of results (4). Additionally, 33% of clinical trials registered on the NIH platform failed to recruit enough participants, leading to incomplete or significantly delayed studies (5).
"TrialMaster" is an artificial intelligence assistant created to aid researchers in the formulation of robust clinical trial protocols. It provides guidance in defining study objectives, methodology, ethical considerations, and advises on the establishment of inclusion and exclusion criteria as well as data management procedures.
The TrialMaster prompt was built using insights from papers on clinical trial design, including work from the Institute of Medicine (USA) (6) and contributions from M. Shi et al. (7), who explore the role of AI in refining clinical trial protocols.
