Breakthroughs in large language models are accelerating the evolution of medical artificial intelligence from standalone models to multi-agent systems. To organize this landscape, we propose a hierarchical taxonomy spanning Level 0 to Level 5. Given that foundational Level 0 to Level 2 architectures face cognitive and collaborative bottlenecks in complex clinical workflows, this survey focuses primarily on the architectural paradigm shift between Level 3 and Level 4. Specifically, we examine how systems transition from static Level 3 collaboration based on predefined roles to Level 4 self-evolving architectures that iteratively refine internal strategies through interactive feedback. Concurrently, we explore governance frameworks required to ensure system safety and trustworthiness under stringent clinical standards. Finally, we highlight that the capacity to dynamically generate agents and reconstruct collaborative topologies based on real-world scenarios will be essential for surpassing current capability limits. Building on this concept, we envision a Level 5 medical AI landscape defined by evolutionary agent swarms. To help researchers continuously track this field, our updated GitHub repository (https://github.com/Tiany1Yu/Medical_Multi_Agent_Systems_Survey_Papers) is publicly available.