Project

Artificial Intelligence and Machine Learning in Clinical Development: a Translational Perspective

PXHere

Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, the computing power to identify clinically-meaningful patterns in the data using efficient artificial intelligence (AI) and machine-learning (ML) algorithms, and regulators willing to embrace this change through new legislation. This perspective summarizes insights and recommendations for a new digital paradigm for healthcare with insights from biotechnology industry, non-profit foundations, regulators, technology companies and academy. Analysis and learning from publically available biomedical and clinical trial datasets, real world evidence from sensors and health records by machine learning architectures are discussed. Strategies for modernizing the clinical drug development process by integration of AI and ML architectures and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing impact of digital algorithmic evidence to improve medical care for patients.

Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, the computing power to identify clinically-meaningful patterns in the data using efficient artificial intelligence (AI) and machine-learning (ML) algorithms, and regulators willing to embrace this change through new legislation. This perspective summarizes insights and recommendations for a new digital paradigm for healthcare with insights from biotechnology industry, non-profit foundations, regulators, technology companies and academy. Analysis and learning from publically available biomedical and clinical trial datasets, real world evidence from sensors and health records by machine learning architectures are discussed. Strategies for modernizing the clinical drug development process by integration of AI and ML architectures and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing impact of digital algorithmic evidence to improve medical care for patients.