Leveraging external data can potentially address many practical issues in clinical trials, such as lack of pediatric patients and patients with rare diseases, cost reduction and efficiency improvement in common diseases, facilitating precision medicine. The Bayesian framework provides a flexible way to integrate external data to improve inference for the study of interest. Bayesian hierarchical model has been proposed based on exchangeability assumptions between external studies and the study of interest. Many data driven, dynamic borrowing methods have since been proposed to determine to the borrowing strength, including power prior, commensurate prior, robust meta analytic predictive prior developed between 2001-2011 and multisource exchangeability model, calibrated BHM, optimal BHM, and elastic prior developed between 2011-2021. The objective of these methods is to encourage information borrowing when historical and trial data are “similar” and refrain from borrowing when they are “different”. In this session, we discuss the new development and application in clinical trials. The session is chaired by Professor Ming-Hui Chen.