Artificial intelligence empowering evidence-based medicine: an L0-L5 evolutionary framework toward personalized precision medicine

Abstract

Evidence-based medicine (EBM) faces inherent challenges in bridging population-based evidence with personalized medical needs. The rapid advancement of artificial intelligence (AI) offers unprecedented opportunities to transform this paradigm. However, applications without theoretical guidance pose risks to the application, regulation and orderly development of AI technologies such as large language models (LLMs). This paper proposes a novel L0-L5 evolutionary framework to systematically guide the integration of LLMs into evidence-based clinical decision-making. The framework delineates a progressive path from current EBM practices (L0) through AI-assisted evidence retrieval (L1), accelerated evidence synthesis (L2), real-world data analysis (L3), digital twin-based personalized evidence (L4), to generative model-driven virtual evidence creation (L5). Each level represents increasing capabilities in addressing the core tensions between evidence timeliness, personalization resolution, and decision transparency. This framework offers a structured approach to harness the transformative potential of LLMs while preserving the fundamental principles of EBM, ultimately enabling truly personalized precision medicine grounded in robust evidence.

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