Life sciences industry is witnessing a paradigm shift of evidence-based approach from population to patient level. This shift is being supported by structured evidence generation methodologies like Systematic Literature Review (SLR), Real World Evidence (RWE), and others to support regulatory, clinical and policy level decision making. The amalgamation of SLR, with real world data and quantitative techniques like Meta-Analysis, Network Meta-Analysis represent highest level of evidence generation. However, the current state of generating SLR, involves manual and labor-intensive process with high dependency on developed countries. The various stakeholders worldwide viz: researchers and policy makers are demanding technology levers to optimize the end-to-end SLR process. As the future of evidence generation will be more complex, industry should invest in smart, intelligent, and connected technology and science-based ecosystem to disrupt and transform existing manual process. This article presents the SLR trends, challenges and potential transformation use case leveraging technology, global capability, and capacity harmonization.
Systematic Literature Review, Real World Evidence, AI/ML Technology, AI/ML Transformation, Harmonization
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