Big-Data-Driven Transformations in Biomedical Research Paradigm - Jianhui LI; Ning YANG (Yixue yu zhexue)
We are currently witnessing a golden era of artificial intelligence for science, marked by a continuous emergence of new algorithms capable of processing diverse biological data sources, and novel tools for uncovering and understanding physiological and pathological knowledge. These advancements are propelling biomedicine towards greater network integration, personalization, and preventive orientation. The paradigm of "data-driven research empowered by artificial intelligence" has become a key strategy for accelerating biomedical discovery. It is reshaping the epistemological frameworks and knowledge production structures of the field by promoting interdisciplinary research models and platform-based organizational forms, thereby fostering global scientific collaboration and transforming the skillsets of biomedical researchers. However, data not only expands the boundaries of cognition, but also creates new epistemic barriers. Now more than ever, biomedicine must respond with caution to the technological and ethical challenges posed by issues, such as medical safety and human–machine value alignment, aiming to avoid adverse clinical outcomes and social impacts.