Technical summary: Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. A perspective led by Dr. Shah, with co-authors from the United States Food and Drug Administration , was published in Nature Digital Medicine that summarized insights, recent developments, and recommendations for infusing actionable computational evidence to accelerate clinical development of life-saving therapies. Other contributors were from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publicly available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures were discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing … View full description
Technical summary: Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. A perspective led by Dr. Shah, with co-authors from the United States Food and Drug Administration , was published in Nature Digital Medicine that summarized insights, recent developments, and recommendations for infusing actionable computational evidence to accelerate clinical development of life-saving therapies. Other contributors were from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publicly available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures were discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration were outlined. The key goal of this collaborations is increasing the impact of novel digital algorithmic evidence to improve medical care for patients.