The reconstruction of the medical research paradigm by artificial intelligence

Since the beginning of the 21st century, artificial intelligence (AI) has been profoundly reshaping medical research, propelling its transition from the traditional "hypothesis-verification" paradigm towards a “data-driven, generative” cognitive structure. Leveraging deep learning and generative pre-trained models, AI is not only transforming research workflows in areas such as literature review, image recognition, clinical trial design, and drug development, but also challenging the philosophical foundations, interpretability, ethical considerations, and evaluation mechanisms of medical research. This paper systematically analyzes the multifaceted evolution of AI’s role in medical research—from a tool to a collaborator, and from an accelerator to a paradigm architect. It proposes that a framework of “trustworthy, transparent, and controllable” AI should serve as the institutional cornerstone for reconstructing future research paradigms. By examining representative case studies and emerging trends under AI’s influence, the paper emphasizes that human-AI collaboration will become the new norm in medical knowledge production. It further calls for establishing interdisciplinary consensus mechanisms to ensure the harmonious progression of scientific rigor, ethical integrity, and innovative capacity in medical research.

Key Words: artificial intelligence; paradigm of medical research; data-driven science; generative pre-training model;  research ethics