Integrating Machine Learning For Risk Prediction and Adaptive Strategy in Drug Development Programs
George Stephen · International Journal on Science and Technology · 2025
The future of clinical development is on the verge of a major transformation due to the convergence of significant 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 the academy, the 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 integrating 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 the impact of digital algorithmic evidence on improving medical care for patients.