The International Congress on Cyberspace Intelligence and Health 2025

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Nan Li
Associate Research Fellow, Deputy Director, Clinical Epidemiology
Research Center, Peking University Third Hospital

Topic: AI推动的循证医学的变革路径展望：从L0到L5
Perspective of AI Empowering Evidence-Based Medicine: An L0-L5 Evolutionary Framework Toward Personalized Precision Medicine

Abstract: 循证医学（EBM）在将群体证据与个体化医疗需求相结合方面面临固有挑战。人工智能（AI）的快速发展为转变这一范式提供了前所未有的机遇。然而，缺乏理论指导的应用可能对大型语言模型（LLM）等AI技术的应用、监管和有序发展带来风险。本研究提出一种新颖的L0-L5演进框架，以系统指导LLM融入循证临床决策。该框架描绘了一条渐进路径：从当前循证实践（L0）出发，依次实现AI辅助证据检索（L1）、加速证据整合（L2）、真实世界数据分析（L3）、基于数字孪生的个体化证据生成（L4），最终达到生成式模型驱动的虚拟证据创建（L5）。每一层级均在解决证据时效性、个体化程度与决策透明度之间的核心矛盾方面展现出递进的能力。该框架为利用LLM的变革潜力提供了一条结构化路径，同时坚守循证医学的基本原则，最终有望实现基于坚实证据的真正个体化精准医疗。

Evidence-based medicine (EBM) faces inherent challenges in bridging population-level evidence with personalized medical needs. The rapid advancement of artificial intelligence (AI) presents unprecedented opportunities to transform this paradigm, yet applications lacking theoretical guidance can hinder the implementation, regulation, and orderly evolution of technologies such as large language models (LLMs). This talk introduces a novel L0-L5 evolutionary framework to systematically integrate LLMs into evidence-based clinical decision-making. The pathway progresses from current EBM practices (L0), through AI-assisted evidence retrieval (L1), accelerated evidence synthesis (L2), real-world data analysis (L3), and digital twin-based personalized evidence generation (L4), culminating in generative model-driven virtual evidence creation (L5). Each level incrementally addresses the tension among evidence timeliness, personalization granularity, and decision transparency. The framework offers a structured approach to harnessing LLMs while safeguarding EBM’s core principles, paving the way for truly personalized precision medicine grounded in robust evidence.

Biography: 李楠，博士，副研究员，硕士研究生导师，北京大学第三医院临床流行病学研究中心副主任。兼任中华医学会临床流行病学和循证医学分会委员及青年学组副组长、中华预防医学会流行病学分会委员、北京医学会临床流行病学和循证医学分会委员兼学术秘书、中国健康促进与教育协会健康教育方法学研究分会副主任委员等学术职务。她担任《Surgical Oncology》《AI in Neurology》《临床药物治疗杂志》《中华脑血管病杂志》《中国当代儿科杂志》《中华流行病学杂志》《Family Medicine and Community Health》等期刊的编委或青年编委，在循证医学与公共卫生领域拥有丰富的科研与实践经验。

Dr. Nan Li is an Associate Research Fellow, Master’s Supervisor, and Deputy Director of the Clinical Epidemiology Research Center at Peking University Third Hospital. She serves on numerous professional committees, including the Chinese Medical Association’s Division of Clinical Epidemiology and Evidence-Based Medicine (Youth Subgroup Deputy Leader), the Chinese Preventive Medicine Association’s Epidemiology Division, and the Beijing Medical Association’s Clinical Epidemiology and Evidence-Based Medicine Division (Academic Secretary), among others. Dr. Li also sits on editorial boards such as Surgical Oncology, AI in Neurology, Journal of Clinical Drug Therapy, Chinese Journal of Cerebrovascular Diseases, Chinese Journal of Contemporary Pediatrics, Chinese Journal of Epidemiology, and Family Medicine and Community Health. Her research focuses on evidence-based medicine, epidemiology, and data-driven healthcare innovation.