Artificial Intelligence for Clinical Trial Design

被引：310

作者：

Harrer, Stefan ^{[1
]}

Shah, Pratik ^{[2
]}

Antony, Bhavna ^{[1
]}

Hu, Jianying ^{[3
]}

机构：

[1] IBM Res, IBM Res Australia Lab, Melbourne, Vic 3006, Australia

[2] MIT, Media Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[3] IBM Res, IBM TJ Watson Res Ctr, Yorktown Hts, NY 10598 USA

来源：

TRENDS IN PHARMACOLOGICAL SCIENCES | 2019年 / 40卷 / 08期

关键词：

SEIZURE PREDICTION; IBM WATSON; BIG DATA; DEEP; SYSTEMS; VALIDATION; SUCCESS; RISK;

D O I：

10.1016/j.tips.2019.05.005

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

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.

引用

页码：577 / 591

页数：15

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