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. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are 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 are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.
Sensor data integration is crucial for the analysis and identification of environmental patterns. In this research project, publicly available datasets of environmental sensor data are integrated with Google Earth. Information on environmental patterns is identified, analyzed, and presented on the Web in response to user queries. This prototype demonstrates how sensor data can be integrated with Google Earth to enable the geographic and temporal context display of environmental data, with an interactive format, for users to analyze and identify environmental patterns in real-time.
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