The Evolution of Medical Evidence Ecosystems.

Abstract

Medical knowledge accumulates exponentially, yet clinical implementation lags behind, creating a widening gap between science and practice. The hierarchical model positioning randomised clinical trials (RCTs) as the sole gold standard is undergoing transformation amid increasing regulatory recognition of real-world evidence (RWE). We apply Vanchurin's multilevel learning theory to interpret the medical evidence system as an adaptive ecosystem of complementary research methods. Within this framework, randomised controlled trials, real-world evidence, and system-level analytical approaches represent interconnected components of dynamic evidence architecture. Rather than competing methodologies, these approaches contribute distinct but complementary forms of knowledge to medical decision-making. Five method integration models are identified: sequential, convergent, synthetic, adaptive, and personalised. Different research methods occupy specific ecological niches, offsetting mutual constraints through the coevolution of key participants: regulators, pharmaceutical companies, physicians, and patients. Successful evolution of the medical evidence system requires coordinated development of all stakeholders, methodological pluralism coupled with scientific rigor, and the establishment of continuous learning infrastructure. The transition to dynamic real-time medicine can be understood as an adaptive response to the gap between knowledge generation and application.