Artificial Intelligence for Clinical Trial Design.

By Stefan Harrer, Pratik Shah, Bhavna Josephine Antony, Jianying Hu. TIPS - Trends in Pharmacological Sciences, 2019

492 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Biophysics, Health Informatics

View Paper: https://www.semanticscholar.org/paper/a20eea154ae75bfc34f1a9f9ac6465a8dff6c224

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In Health Informatics

1. Rohan Gupta
2. Devesh Srivastava
3. Mehar Sahu
4. S. Tiwari
5. R. K. Ambasta
6. Pravir Kumar

Citations in the corpus

• Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector (2023, unknown): “This is not to say that providing high-quality healthcare is simple, but it does imply that we have some alternatives [5] to create simpler mechanisms that will offer better care and benefit everyone.”
• Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Furthermore, after the introduction of AI technology, the success rates of clinical trials have improved drastically [453].”
• Artificial intelligence in drug discovery and development (2020, unknown): “101 Harrer, S. et al. (2019) Artificial intelligence for clinical trial design.”