JMIR Publications

ABSTRACT

As AI agents become increasingly capable of autonomous action in health care, a prerequisite remains underaddressed: the persistent, structured memory that makes such action contextually meaningful. Clinicians face cognitive overload not from any single task but from the erosion of decision context over time. Existing tools—personal knowledge management frameworks, LLM built-in memory, and autonomous agents—each address parts of this problem but leave gaps in auditability, portability, or contextual persistence. This Viewpoint argues that memory should precede action: before AI agents can act meaningfully, they need persistent, human-controlled context. We describe externalized living memory—a structured knowledge base that both human and AI can read and write—as it emerged from the first author's practice as a cardiovascular radiologist and division chief. The approach is organized as a layered architecture with a routing table for scalable context loading and a governance hierarchy for sustainable maintenance. We illustrate the approach through clinical vignettes, compare it with existing solutions, and discuss limitations including the small-team evidence base and maintenance costs. An open-source implementation with templates and setup instructions accompanies this paper.