Artificial intelligence applied to clinical trials: opportunities and challenges
Open accessAbstract
Background Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. Methods Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities' documents. Results Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval. Conclusion The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.
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AI is limited by the data it is trained on and an inability to account for the nuanced contexts of individual research studies.
“However, AI is limited by the data it is trained on and an inability to account for the nuanced contexts of individual research studies [2].”
From Leveraging AI Large Language Models for Writing Clinical Trial Proposals in Dermatology: Instrument Validation Study (2026) · cites this paper as [2]
The Results and Discussion > Design sections identify incomplete or poor-quality datasets as limits on AI applications, supporting the data-dependent part of the claim. The paper also discusses model interpretation and validation, but does not say AI is unable to account for the nuanced contexts of individual research studies. Page numbers were not available in our copy. (LLM, conf=0.86)
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- [29]Weissler EH, Naumann T, Andersson T, et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021;22(1). 10.1186/s13063-021-05489-x. PMC8365941 34399832 · DOI
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- Authors
- Askin, S., Burkhalter, D., Calado, G., & El Dakrouni, S.
- Affiliations
- Novartis (Switzerland) (CH)
- Massachusetts College of Pharmacy and Health Sciences (US)
- Corresponding author
- Askin, Scott
- Venue
- Health and Technology
- Published
- 2023-02-28
- Volume
- 13
- Issue
- 2
- Pages
- 203-213
- ISSN
- 2190-7188 (ISSN-L)
- 2190-7196
- PMC
- PMC9974218
- OpenAlex
- W4322621757
- Cited by
- 285
- References
- 36
- Citations by year
- 2026: 61 · 2025: 123 · 2024: 83 · 2023: 18
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Health and Technology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in clinical trials, machine learning, adaptive clinical trials, clinical trial recruitment, cancer clinical trials, ethical challenges of AI, data availability, drug development, personalized medicine, trial efficiency
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