21世纪以来，人工智能（artificial intelligence，AI）正在以前所未有的深度介入医学科研，推动其从“假设—验证”范式向“数据驱动—生成式”的认知结构跃迁。借助深度学习与生成式预训练模型，AI不仅正在文献综述、图像识别、临床试验设计、药物开发等方面重塑科研流程，更对医学研究的哲学基础、可解释性、科研伦理与评价机制提出了挑战。本文系统分析了AI在医学科研中从工具到协作者、从加速器到范式建构者的多重角色变迁，提出“可信、透明、可控”的AI框架应成为未来科研范式重构的制度基石。通过梳理AI介入下的典型案例与趋势，强调人机协同将成为医学知识生产的新常态，并呼吁构建跨学科共识机制，以确保医学科研的科学性、伦理性与创新性的协同进化。

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.