MedBeads: An Agent-Native, Immutable Data Substrate for Trustworthy Medical AI

ArXi:2602.01086v2 Announce Type: replace Background: As of 2026, Large Language Models (LLMs) nstrate expert-level medical knowledge. However, deploying them as autonomous "Clinical Agents" remains limited. Current Electronic Medical Records (EMRs) and standards like FHIR are designed for human review, creating a "Context Mismatch": AI agents receive fragmented data and must rely on probabilistic inference (e.g., RAG) to reconstruct patient history. This approach causes hallucinations and hinders auditability.