Traditional evidence-based medicine has been essential in medical practice and health decision-making. However, it has also continuously exposed shortcomings such as low efficiency in evidence generation, narrow scope of coverage, and imperfect integration strategies, making it challenging to serve clinical diagnosis and treatment and regulatory decision-making. Therefore, it is urgent to adapt to the development of cutting-edge technology and to expand and improve the concept of evidence-based medicine. Deep evidence-based medicine proposed in 2023 aims to advocate the innovative use of the latest artificial intelligence and natural language processing technologies, comprehensively expanding the breadth, depth, and integrability of evidence and improving the efficiency of evidence generation and integration. Building a new generation of evidence-based decision-making ecosystems based on deep evidence-based medicine has broad prospects for practical application. It can promote the development of evidence retrieval, generation, integration, dissemination, transformation, and application, deeply explore imaging, multi-omics, and real-world data to increase the utilization potential of real-world evidence, establish dynamic literature management platforms and decision support tools, reduce resource waste, and promote evidence flow. Utilizing this system can help obtain individual-centered comprehensive clinical evidence and play a significant role in talent training, reforming evidence-based teaching, popularizing science, and ultimately promoting the goal of "One Health".

Deep evidence-based medicine; Evidence-based decision ecosystem; Evidence integration; Randomized controlled trials