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Review
. 2023;13(2):203-213.
doi: 10.1007/s12553-023-00738-2. Epub 2023 Feb 28.

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
Review

Artificial Intelligence Applied to clinical trials: opportunities and challenges

Scott Askin et al. Health Technol (Berl). 2023.

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.

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Conflict of interest statement

Conflict of InterestThe authors are all employees sponsored by their employer to undertake the Regulatory Affairs and Health Policy Master’s Degree program, for which this paper was developed. Two authors are shareholders of the company for which they are employed.

Figures

Fig. 1
Fig. 1
Flow Diagram of Papers Researched
Fig. 2
Fig. 2
Number of papers referring to AI applications, per categorized CT activity This graph represents the application of AI across the categories of CT activities defined, as discussed within the publications reviewed. Out of a total of 48 papers that were in scope, 38 papers described application of AI to a single activity of a clinical trial, five papers described two activities, three papers described three activities, and the remaining two papers described four activities
Fig. 3
Fig. 3
Number of papers referring to AI applications per therapeutic area [6] This graph represents the application of AI across therapeutic areas (TA), as discussed in the publications reviewed. Out of a total of 48 papers that were in scope, 26 papers did not describe a specific TA. The distribution of TAs across the remaining 22 papers is represented in this graph, which include three papers that described two TAs within the same paper
Fig. 4
Fig. 4
Summary of the key opportunities, challenges and implications of AI, per research activity of a clinical trial
See this image and copyright information in PMC

References

    1. C K, KW J, WW T. How artificial intelligence could redefine clinical trials in cardiovascular medicine: lessons learned from oncology. Personalized Med. 2019;16(2):87–92. doi: 10.2217/PME-2018-0130. - DOI - PubMed
    1. S H, P S, B A, J H. Artificial Intelligence for Clinical Trial Design.Trends in pharmacological sciences. 2019;40(8):577–591. doi:10.1016/J.TIPS.2019.05.005 - PubMed
    1. Delso G, Cirillo D, Kaggie JD, Valencia A, Metser U, Veit-Haibach P. How to design AI-Driven clinical trials in Nuclear Medicine. Semin Nucl Med. 2021;51(2):112–9. doi: 10.1053/J.SEMNUCLMED.2020.09.003. - DOI - PubMed
    1. C G, N M. The missing pieces of Artificial Intelligence in Medicine. Trends Pharmacol Sci. 2019;40(8):555–64. doi: 10.1016/J.TIPS.2019.06.001. - DOI - PubMed
    1. M AG. Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool. BMC Med Res Methodol. 2021;21(1). 10.1186/S12874-021-01354-2. - PMC - PubMed

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