use cases

Clinical research

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).

Why it matters

  • 33% of clinical trials have issues with randomization, blinding, or statistical analysis, affecting result validity.
  • 33% of clinical trials fail to recruit enough participants, causing delays or incomplete studies.
  • Challenges in patient selection, participant retention, data collection and analysis, time to results, and ethical considerations significantly impact clinical study development.


"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.

User person: Principal Investigator, Clinical Trial Coordinator, Clinical Project Manager, Regulatory Affairs Specialist, Director of Research and Development.

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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.


  1. National Institutes of Health (NIH). Clinical Trials. Disponible en: Accedido el 23 de febrero de 2023.
  2. Food and Drug Administration (FDA). Clinical Trials: What Patients Need to Know. Disponible en: Accedido el 23 de febrero de 2023.
  3. World Health Organization (WHO). Clinical trials. Disponible en: Accedido el 23 de febrero de 2023.
  4. Chan AW, Hróbjartsson A, Haahr MT, Gøtzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA. 2004 May 26;291(20):2457-65. doi: 10.1001/jama.291.20.2457. PMID: 15161896.
  5. Olaniyan T, Jeebhay M, Röösli M, Naidoo R, Baatjies R, Künzil N, Tsai M, Davey M, de Hoogh K, Berman D, Parker B, Leaner J, Dalvie MA. A prospective cohort study on ambient air pollution and respiratory morbidities including childhood asthma in adolescents from the western Cape Province: study protocol. BMC Public Health. 2017 Sep 16;17(1):712. doi: 10.1186/s12889-017-4726-5. PMID: 28915873; PMCID: PMC5602849.
  6. Institute of Medicine (US) Forum on Drug Discovery, Development, and Translation. Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary. Washington (DC): National Academies Press (US); 2010. 2. Miller, JE. Institutional Review Board: Management and Function. Jones and Bartlett Publishers; 2005.
  7. M. Shi et al., "The role of artificial intelligence in improving clinical trial protocol design: a systematic review," npj Digital Medicine, vol. 3, no. 1, Aug. 2020, Art. no. 96. DOI: 10.1038/s41746-020-00306-4.

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