Healthcare AI carries higher stakes than most domains, and the constraints show up early. HIPAA compliance shapes the architecture, ICD-10 and RxNorm terminology demand accuracy that generic models do not provide, and clinical validation is not optional.
That pushes design decisions in a specific direction: domain tuned embeddings over general purpose ones, encrypted data handling by default, and retrieval pipelines whose sources a clinician can audit. The goal is not to replace clinical judgement but to surface accurate context faster.
Responsible healthcare AI is mostly disciplined engineering: clear provenance, conservative failure modes, and a refusal to present a guess as a fact.