Vol. 4 No. 1 (2024): Journal of Machine Learning for Healthcare Decision Support
Articles

Optimizing Drug Discovery and Development with AI-Powered Clinical Trials Management: Applies AI algorithms to optimize the design and execution of clinical trials, accelerating drug discovery and development processes in the pharmaceutical industry

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  • Dr. Jacek Kowalski
Dr. Jacek Kowalski
Associate Professor of Artificial Intelligence, University of Wrocław, Poland
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Published 17-05-2024

Keywords

  • Clinical trials,
  • AI,
  • drug discovery,
  • optimization,
  • patient recruitment,
  • trial design,
  • data analysis,
  • pharmaceutical industry,
  • drug development
    • ...More
    Less
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
D. J. Kowalski, “Optimizing Drug Discovery and Development with AI-Powered Clinical Trials Management: Applies AI algorithms to optimize the design and execution of clinical trials, accelerating drug discovery and development processes in the pharmaceutical industry”, Journal of Machine Learning for Healthcare Decision Support, vol. 4, no. 1, pp. 60–71, May 2024, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/16

Abstract

Clinical trials are a crucial component of the drug discovery and development process, providing evidence of a drug's safety and efficacy before it can be approved for use. However, the traditional approach to designing and conducting clinical trials is often time-consuming, costly, and inefficient. In recent years, there has been a growing interest in applying artificial intelligence (AI) algorithms to optimize various aspects of clinical trials, with the goal of accelerating the drug development process. This paper provides an overview of the use of AI-driven approaches to optimize clinical trials, including patient recruitment, trial design, site selection, and data analysis. We discuss the potential benefits of these approaches, such as reducing costs, shortening timelines, and improving patient outcomes. We also highlight some of the challenges and limitations of AI-driven clinical trials optimization and provide recommendations for future research directions.

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