Convergence of massive new digital data sources, the computing power to find clinically meaningful patterns in the data using effective artificial intelligence and machine-learning algorithms, and regulators welcoming this change through new partnerships, the future of clinical development is about to undergo a significant transformation. This viewpoint compiles knowledge, new advancements, and suggestions from the biotechnology sector, academic institutions, nonprofit organizations, government agencies, and tech companies for integrating relevant computational evidence into clinical research and healthcare. There is discussion of machine-learning architectures' analysis and learning of publicly accessible biological and clinical trial data sets, real-world evidence from sensors, and medical records. Using newly established regulatory paths at the US Food and Drug Administration, strategies for modernizing the clinical development process by integrating digital methodologies based on AI and ML and secure computing technologies are described. We wrap off by going into the uses and effects of digital algorithmic evidence to help patients get better medical treatment.