[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4022":3,"related-4022":63},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":42,"slug":45,"view_count":15,"doi":46,"paper":47,"created_at":62},4022,"Evolution of evidence-based medicine: toward digital evidence ecosystems and artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.63528\u002Fjebp.2026.00016","Evidence-based medicine (EBM) has transformed clinical decision-making by integrating the best available research evidence with clinical expertise and patients’ values and preferences. Over the past three decades, EBM has evolved from the critical appraisal of individual studies into a broader framework encompassing evidence synthesis, clinical practice guidelines, assessment of evidence certainty, research transparency, and shared decision-making. Despite these advances, contemporary EBM faces important challenges, including the rapidly increasing volume of research, delays in evidence synthesis and implementation, limited applicability of randomized controlled trials to heterogeneous real-world populations, and difficulties in individualizing population-level evidence. Emerging approaches—including real-world evidence, living evidence, learning health systems, precision medicine, and artificial intelligence (AI)—offer opportunities to address these limitations. Together, these approaches may enable a transition from static to continuously updated evidence, from population-average to more personalized evidence, and from a linear evidence pipeline to a learning evidence ecosystem in which clinical practice both uses and generates evidence. AI may further accelerate evidence retrieval, synthesis, updating, and individualized decision support, while introducing challenges related to reliability, bias, transparency, reproducibility, and accountability. Next-generation EBM should therefore be conceptualized not as a replacement for traditional EBM but as its evolution into a digitally connected, continuously learning evidence ecosystem. In the AI era, the foundational principles of EBM—source verification, critical appraisal, uncertainty assessment, integration of patient preferences, and accountable human judgment—will become increasingly important.","循证医学（evidence-based medicine, EBM）通过将最佳可得研究证据与临床专业知识及患者的价值观和偏好相结合，改变了临床决策。过去三十年间，EBM已从对单项研究的批判性评价，演变为涵盖证据合成、临床实践指南、证据确定性评估、研究透明度以及共享决策的更广泛框架。尽管取得了这些进展，当代EBM仍面临重要挑战，包括研究数量迅速增长、证据合成与实施延迟、随机对照试验对异质性真实世界人群的适用性有限，以及群体层面证据难以个体化应用。新兴方法——包括真实世界证据、动态证据、学习型卫生系统、精准医学和人工智能（AI）——为解决这些局限提供了机遇。这些方法共同有望推动从静态证据向持续更新证据的转变，从群体平均证据向更个性化证据的转变，以及从线性证据 pipeline 向学习型证据生态系统的转变，在该系统中临床实践既使用证据也生成证据。AI可进一步加速证据检索、合成、更新和个体化决策支持，同时带来与可靠性、偏倚、透明度、可重复性和问责制相关的挑战。因此，下一代EBM不应被概念化为对传统EBM的替代，而应被视为其向数字互联、持续学习的证据生态系统的演进。在AI时代，EBM的基本原则——来源核实、批判性评价、不确定性评估、患者偏好整合以及可问责的人类判断——将变得愈发重要。",null,"Journal of Evidence-Based Practice","2026-09-29T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文探讨循证医学向数字证据生态与AI演进，属医学信息学领域，与三农、农业信息化、智慧农业无关，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"循证医学","数字证据生态","医疗人工智能","学习型健康系统",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":41},[32],{"code":33,"score":34,"supported":35,"title":36,"abstract":38,"tags":40},"I2",14,true,[37],"artificial intelligence",[37,39],"decision support",[],"农业相关性需要复核",[43,44],"循证医学 数字证据生态","人工智能 证据合成","循证医学数字证据生态-4022","10.63528\u002Fjebp.2026.00016",{"doi":46,"openalex_id":48,"authors":49,"venue":10,"cited_by_count":15,"oa_url":53,"card":54,"direction":60,"ingested_from":61},"W7214796268",[50],{"name":51,"orcid":52},"Soo Young Kim","https:\u002F\u002Forcid.org\u002F0000-0002-3205-9408","http:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs13073-016-0388-7.pdf",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"综述循证医学向数字证据生态与AI驱动学习型体系演进的方向与挑战。","文献综述，整合真实世界证据、活证据、学习型医疗系统与AI。","AI可加速证据合成与个体化决策，但需坚守溯源、批判评价与问责原则。","农业人工智能与决策模型","可借鉴数字证据生态理念，构建农业领域持续学习、可溯源的智能决策证据体系。","农业遥感与作物表型","openalex","2026-10-02T23:30:23.382009Z",{"total":15,"page":64,"page_size":64,"items":65},1,[]]