Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review
Open accessAbstract
Clinical trials are time consuming, expensive, and often burdensome on patients. Clinical trials can fail for many reasons. This survey reviews many of these reasons and offers insights on opportunities for improving the likelihood of creating and executing successful clinical trials. Literature from the past 30 years was reviewed for relevant data. Common patterns in reported successful trials are identified, including factors regarding the study site, study coordinator/investigator, and the effects on participating patients. Specific instances where artificial intelligence can help improve clinical trials are identified.
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It has been suggested that highly specific inclusion criteria may result in difficulties identifying suitable participants, especially for low prevalence conditions and hard-to-reach populations.
“It has been suggested that highly specific inclusion criteria may result in difficulties identifying suitable participants, especially for low prevalence conditions and hard-to-reach populations [55].”
Recruitment challenges, low adherence, and high rates of attrition commonly compromise randomised controlled trials.
“Recruitment challenges, low adherence, and high rates of attrition commonly compromise randomised controlled trials [55, 56]”
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Fundamentals of Computational Intelligence
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The Life Cycle and Management of Protocol Deviations
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0398 ARE CLINICAL TRIAL PARTICIPANTS REPRESENTATIVE FOR PATIENTS WITH INSOMNIA?
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Flagged first, then by citations
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- [61]Institute of Medicine Public Engagement and Clinical Trials: New Models and Disruptive Technologies. Work Shop Summary 2012 The National Academies Press Washington, DC 22514814
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Artificial intelligence in drug discovery and development
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Human organs-on-chips for disease modelling, drug development and personalized medicine
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
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Artificial intelligence for clinical trial design
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Population-based high-throughput toxicity screen of human iPSC-derived cardiomyocytes and neurons
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Hawke, L. J., Nelson, E., O'Brien, P., Crossley, K. M., Choong, P. F., Bunzli, S., & Dowsey, M. M. (2024) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2024.101283 · Cited by 22 · see citing works
Saady, M., Eissa, M., Yacoub, A. S., Hamed, A. B., & Azzazy, H. M. E.-S. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103066 · Cited by 15 · see citing works
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- Authors
- Fogel, D. B.
- Corresponding author
- Fogel, David B.
- Venue
- Contemporary Clinical Trials Communications
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 11
- Pages
- 156-164
- ISSN
- 2451-8654 (ISSN-L)
- PMC
- PMC6092479
- OpenAlex
- W2885447570
- Cited by
- 1,035
- References
- 123
- Citations by year
- 2026: 115 · 2025: 156 · 2024: 198 · 2023: 167 · 2022: 142 · 2021: 164 · 2020: 68 · 2019: 22 · 2018: 2
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Contemporary Clinical Trials Communications (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
- Keywords
- clinical trial failure, study coordinators, patient burden, artificial intelligence in clinical trials
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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