And bibliometric analysis of deep learning in medicine, mapping the field’s growth, trends, and impact from 2008 to 2023. Based on data from Scopus, the paper analyses the evolution of deep learning in medicine, identifying trends in publications, authors, institutions, and countries. Deep learning represents a powerful research paradigm for machine learning, with an exponential rise in publications and patents since 2010, fueled by improvements in computational power, access to large scale datasets, and increasingly advanced neural network architectures. The results highlight the cross-border aspect of deep learning research with contributions from China, the United States, and Europe. AI plays a critical role in medical imaging, diagnostics, and predictive analytics with an emerging scope in public health and personalized medicine. This study shows that deep learning can help researchers identify contemporary healthcare issues and design their research to address these issues.