Data Science, AI, and Machine Learning in Drug Development

The confluence of big data, AI, and ML has revolutionized drug development and healthcare delivery. Data science has the potential to lead this transformative change by harnessing data from diverse sources and leveraging digital technologies and advanced analytics to enable data-driven decisions. This book provides a comprehensive review of challenges and opportunities, regulatory developments, and real-world examples of AI-powered solutions in drug R&D.

Format: Hardback
Length: 320 pages
Publication date: 03 October 2022
Publisher: Taylor & Francis Ltd

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has brought about a revolutionary transformation in the development and delivery of healthcare. To fully harness the potential of these cutting-edge technologies, it is crucial to systematically collect and analyze data from diverse sources, leveraging digital technologies and advanced analytics to make data-driven decisions. At this pivotal juncture, data science holds the key to spearheading this transformative change.

Data Science, AI, and Machine Learning in Drug Research and Development serves as a comprehensive resource, intended to be a single source of information on the evolving landscape of drug R&D. It delves into emerging applications of big data, AI, and ML in drug development, as well as the establishment of robust data science organizations to facilitate biopharmaceutical digital transformations.

The book offers a comprehensive review of the challenges and opportunities associated with the applications of big data, AI, and ML in the entire spectrum of drug R&D. It discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval, providing a balanced approach to data science organization build. Additionally, it presents real-world examples of AI-powered solutions to various issues throughout the drug development lifecycle, offering sufficient context for each problem and providing detailed descriptions of solutions that are accessible to practitioners with limited data science expertise.

By leveraging the power of big data, AI, and ML, the pharmaceutical industry can expedite drug discovery, improve patient outcomes, and reduce healthcare costs. Data science plays a pivotal role in unlocking the full potential of these technologies, enabling researchers and practitioners to make informed decisions and drive innovation in the healthcare sector. As the field of data science continues to evolve, it is poised to lead the way

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has brought about a revolutionary transformation in the development and delivery of healthcare. To fully harness the potential of these cutting-edge technologies, it is crucial to systematically collect and analyze data from diverse sources, leveraging digital technologies and advanced analytics to make data-driven decisions. At this pivotal juncture, data science holds the key to spearheading this transformative change.

Data Science, AI, and Machine Learning in Drug Research and Development serves as a comprehensive resource, intended to be a single source of information on the evolving landscape of drug R&D. It delves into emerging applications of big data, AI, and ML in drug development, as well as the establishment of robust data science organizations to facilitate biopharmaceutical digital transformations.

The book offers a comprehensive review of the challenges and opportunities associated with the applications of big data, AI, and ML in the entire spectrum of drug R&D. It discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval, providing a balanced approach to data science organization build. Additionally, it presents real-world examples of AI-powered solutions to various issues throughout the drug development lifecycle, offering sufficient context for each problem and providing detailed descriptions of solutions that are accessible to practitioners with limited data science expertise.

By leveraging the power of big data, AI, and ML, the pharmaceutical industry can expedite drug discovery, improve patient outcomes, and reduce healthcare costs. Data science plays a pivotal role.

Dimension: 234 x 156 (mm)
ISBN-13: 9780367708078