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Cited by 285Artificial Intelligence in Healthcare and EducationStatistical Methods in Clinical TrialsArtificial Intelligence Applications

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Artificial Intelligence Applied to clinical trials: opportunities and challenges

Open access
Askin, Scott, Burkhalter, Denis, Calado, Gilda, El Dakrouni, Samar·2023·Health and Technology
DOI 10.1007/s12553-023-00738-2PMC PMC9974218
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Abstract

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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Research works printed but not linked: 2

  1. 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). https://doi.org/10.1186/s13063-021-05489-x. · DOI
  2. European Commission’s Heads of Medicines Agency. Joint HMA/EMA workshop on artificial intelligence in medicines regulation | European Medicines Agency. Published 2021. Accessed October 17., 2021. https://www.ema.europa.eu/en/events/joint-hmaema-workshop-artificial-intelligence-medicines-regulation

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Metadata

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
Publisher
Springer Science and Business Media LLC
Published
2023-02-28
Volume
13
Issue
2
Pages
203-213
ISSN
  • 2190-7188 (ISSN-L)
  • 2190-7196
DOI
10.1007/s12553-023-00738-2
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
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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