Artificial Intelligence Applied to clinical trials: opportunities and challenges

• Published in: Health and technology Vol. 13; no. 2; pp. 203 - 213
• Main Authors: Askin, Scott, Burkhalter, Denis, Calado, Gilda, El Dakrouni, Samar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-03-2023; Springer Nature B.V
• Subjects: Artificial intelligence; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Clinical trials; Collaboration; Computational Biology/Bioinformatics; Documents; Drug development; Engineering; Ethical standards; Evolution; Machine learning; Medicine/Public Health; Oncology; Pharmaceutical industry; R & D/Technology Policy; Regulatory agencies; Regulatory approval; Review Paper; Statistical analysis; United States > US; Machine learning (ML); Clinical trials (CT); Artificial Intelligence (AI); Opportunities; Challenges; Implications
• ISSN: 2190-7188; 2190-7196
• DOI: 10.1007/s12553-023-00738-2

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.