AI-Powered Clinical Trial Design With Translational Bioinformatics

Author:

Mittal Shashank1,Singh Priyank Kumar2ORCID,Gochhait Saikat3ORCID,Gaur Nisha4ORCID,Kumar Shubham5

Affiliation:

1. O.P. JIndal Global University, India

2. Doon University, Dehradun, India

3. Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed), Pune, India & Samara State Medical University, Russia

4. Gautam Buddha University, India

5. University of Minnesota, Minneapolis, USA

Abstract

Clinical trial design is undergoing a revolution fueled by artificial intelligence (AI) and translational bioinformatics. This chapter explores how AI techniques like machine learning and deep learning are being harnessed to analyze vast datasets of biological and clinical information. By integrating these insights with translational bioinformatics, researchers can identify promising drug candidates, select patients most likely to benefit from treatment, and design more efficient and targeted clinical trials. Real-world examples showcase the application of AI in immuno-oncology patient selection, drug discovery for rare diseases, predicting Alzheimer's trial outcomes, and virtual patient recruitment for cardiovascular studies. While challenges like data quality and ethical considerations exist, AI and translational bioinformatics hold immense promise for accelerating drug development, bringing life-saving therapies to patients faster.

Publisher

IGI Global

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