"Clinical Trials (CTs) remain the foundation of safe and effective drug development." This is how this paper by Scott Askin et al. begins. The implications are well known in all aspects and processes, from those inherent to the development of new drugs to the analysis of the results obtained by CTs. In this context, the authors' proposal to conduct a literature search on the use of AI in CTs, providing an overview of the use of this technology and tools from a comprehensive perspective, is very interesting. There is ample literature on the use of AI in each of the items analyzed in the paper (pre-clinical, design, recruitment, conduct, and analysis). What I want to highlight as outstanding aspects when reading this paper, considering CTs as a whole, is the need for a greater amount of data where I would dare to say that a paradigm shift is required regarding the ownership and the advantages of sharing or not sharing information, and the need to adapt regulations to this new reality of AI use that is already established and whose only way is to continue growing.
Artificial Intelligence Applied to clinical trials: opportunities and challenges
Scott Askin 1 2, Denis Burkhalter 1 2, Gilda Calado 1 3, Samar El Dakrouni 1 4
Affiliations expand
- PMID: 36923325
- PMCID: PMC9974218
- DOI: 10.1007/s12553-023-00738-2
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.
Keywords: Artificial Intelligence (AI); Challenges; Clinical trials (CT); Implications; Machine learning (ML); Opportunities.
