Clinical AI copilots and health assistants are specialized generative‑AI tools that help clinicians capture, summarize, and structure patient encounters, automate coding and billing tasks, and orchestrate routine clinical workflows. This topic focuses on clinical documentation tools—assistants that integrate with electronic health records (EHRs), clinical communications, and office productivity suites to reduce administrative burden and improve data quality. As of March 2026, adoption is driven by more capable multimodal models, tighter platform integrations, and heightened expectations for safety, privacy, and auditability. Leading platform approaches covered here include: Microsoft 365 Copilot for embedding assistant capabilities across Word, Outlook, Teams and other productivity apps; IBM watsonx Assistant for enterprise virtual agents and no‑code developer workflows; Google’s Gemini and Vertex AI for multimodal models and end‑to‑end model lifecycle management; Cohere for private, customizable LLMs and embeddings; Kore.ai for orchestrating multi‑agent workflows with governance and observability; and Anthropic’s Claude family for conversational and analytic assistants. Key trends to evaluate are retrieval‑augmented generation for accurate clinical context, fine‑tuning or private model deployments for PHI protection, multimodal inputs (voice, images, notes) for richer scribing, and platform features that support compliance (audit logs, role‑based access, data residency). Health systems must balance productivity gains with clinical safety, explainability, and regulatory constraints (HIPAA, GDPR, local guidance). This topic helps teams compare capabilities and operational tradeoffs when selecting clinical AI copilots for documentation and workflow automation.