Abstract: Future of clinical development and biomedical research is undergoing a major transformation due to convergence of large new digital datasets, computing power to identify clinically and biologically meaningful inferences using explainable and interpretable Artificial Intelligence (AI) and deep-learning models, and universities embracing this change through new research efforts. However, uncertainties in regulatory requirements, risk aversion, and skepticism about rapidly emerging, yet largely unproven, technologies (such as deep learning), and lack of testable hypotheses and statistical benchmarking of novel computational evidence for clinical decision making remains a major barrier for adoption. Co-development of efficient, fair, interpretable and translational computer science models and statistical methods will result in generating fundamental insights in biology to help physicians and regulators to increase health and longevity of patients without compromising safety and efficacy. This talk will provide an overview and strategic insights for benchmarking novel computational methods + a proposed clinical process for validation of  Software as a Medical Device (SaMD) to de-risk and explain real world evaluation of deep learning models and evidence generated by them. Current status of  real-world evaluations using clinical trials, and strategies for regulation and de risking of emerging technologies and their positive and negative impact on patients will be one of the key learning outcomes of the talk.