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Trends Pharmacol Sci. 2019 Aug;40(8):577-591. doi: 10.1016/j.tips.2019.05.005. Epub 2019 Jul 17.

Artificial Intelligence for Clinical Trial Design.

Author information

1
IBM Research, IBM Research Australia Lab, 3006 Melbourne, VIC, Australia. Electronic address: sharrer@au.ibm.com.
2
Massachusetts Institute of Technology, Media Lab, 02139 Cambridge, MA, USA.
3
IBM Research, IBM Research Australia Lab, 3006 Melbourne, VIC, Australia.
4
IBM Research, IBM T.J. Watson Research Center, 10598 Yorktown Heights, NY, USA.

Abstract

Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.

KEYWORDS:

artificial intelligence; cohort selection; machine learning; patient monitoring; patient recruitment; trial design

PMID:
31326235
DOI:
10.1016/j.tips.2019.05.005
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