数智循证医学: AI时代循证研究与实践新方向

人工智能技术的快速发展, 特别是其在处理复杂医学数据方面的突破性能力, 正在重塑医学证据的生成、 整合与应用模式. 传统循证医学(evidence-based medicine, EBM)虽然在过去30年中显著提升了医学决策的科学性, 但其固有的时滞性、证据孤岛化以及个体化不足等局限性, 在当前医学复杂性急剧增加的背景下愈发凸显. 团队 提出“数智循证医学”(digital intelligent evidence-based medicine, i-EBM)这一新型现代医学范式构想, 其核心特征体 现在三个维度: 整合性(integrated)的多源数据融合、智能化(intelligent)的证据分析与生成、个体化(individualized) 的精准决策支持. i-EBM并非对传统EBM的颠覆, 而是其在人工智能时代的必然演化, 旨在通过机器智能与人类专 业知识的深度协同, 构建一个动态、精准、高效的新一代医学证据体系. 同时, 对于中医药复杂干预特征与辨证论 治规律的量化研究等问题, i-EBM可在数据整合、模式识别与因果推断等方面提供支持, 为揭示中医药证据内在 规律与优化循证评价体系提供潜在的工具与思路.

Evidence-based medicine (EBM), formalized in the 1990s, has redefined clinical practice by advocating the integration of research evidence, clinical expertise, and patient values. This paradigm has introduced methodological rigor through randomized controlled trials (RCTs) to establish causality, systematic reviews to synthesize findings, and the GRADE approach to evaluate evidence based on risk of bias, inconsistency, indirectness, imprecision, and publication bias. These advancements have shaped clinical guidelines, reduced practice variability, and influenced medical education toward evidence-based inquiry. Despite its contributions, EBM faces challenges in the evolving landscape of modern medicine. The lengthy process of evidence generation, often requiring years for trials and guideline updates, limits responsiveness to emerging health needs, as observed during the COVID-19 pandemic. The external validity of RCT results is constrained by strict inclusion criteria, posing difficulties in applying findings to diverse patient populations with comorbidities. Additionally, the siloed nature of evidence complicates comprehensive care for multifactorial conditions, while the annual influx of over one million medical publications overwhelms traditional synthesis methods. Artificial intelligence (AI) presents a promising avenue to address these issues, leveraging capabilities in processing heterogeneous data. Natural language processing may enhance literature analysis, machine learning could identify patterns in complex datasets, and causal inference might improve the reliability of observational data insights. These technologies hold potential to accelerate evidence development and tailor it to individual needs. This paper proposes digital intelligent evidence-based medicine (i-EBM) as a conceptual evolution of EBM, designed for the AI era. i-EBM envisions a three-layered framework. The data foundation layer aims to integrate structured evidence from RCTs, domain knowledge such as biomedical ontologies and traditional Chinese medicine principles, and multimodal patient data, including electronic health records, genomics, and wearable device outputs. Knowledge graphs are proposed to link these elements into a unified, computable knowledge network. The intelligent processing layer seeks to apply AI for evidence retrieval, data extraction, quality assessment, and synthesis, potentially using large language models to assist these processes. The knowledge service layer intends to provide dynamic guidelines and individualized predictions, supported by ongoing human-machine collaboration to ensure clinical relevance and ethical considerations. i-EBM has the potential to mitigate EBM’s limitations by facilitating real-time evidence updates, reducing knowledge fragmentation through integrated data, and offering personalized decision support. For instance, it may support precision medicine by connecting diverse data sources, with applications possibly extending to fields like oncology or traditional Chinese medicine. Future research could explore autonomous AI systems, optimized clinical workflows, and governance frameworks to address data privacy, bias, and global standardization. In conclusion, i-EBM offers a theoretical framework to extend EBM principles, harnessing AI’s potential alongside human expertise to advance medical research and practice. Meanwhile, for issues such as the quantitative study of the complex intervention characteristics and syndrome differentiation patterns of traditional Chinese medicine, i-EBM can provide methodological support in data integration, pattern recognition, and causal inference, offering potential tools and insights for uncovering the intrinsic regularities of TCM evidence and optimizing its evaluative framework.