This topic examines AI platforms used in healthcare across four practical categories—clinical documentation tools, edge AI vision platforms, AI data/platforms for model development and deployment, and regulatory/compliance tooling. Demand for automated documentation, rapid on‑device imaging inference, and reliable predictive models has grown alongside stricter regulatory expectations and institutional risk management in 2026. Key capabilities sought are clinical-grade model performance, privacy-preserving data handling, deployment at the edge for low-latency imaging, and centralized governance for validation and monitoring. Representative platforms illustrate this ecosystem: Vertex AI provides an end‑to‑end managed ML and GenAI stack for model training, evaluation and scalable deployment (including production and monitoring), making it a common choice for AI data and MLOps needs. IBM watsonx Assistant targets clinical documentation and workflow automation with no‑code and developer options for virtual agents and assistant orchestration. Mistral AI offers open/efficient foundation models and an enterprise production stack focused on efficiency and privacy, relevant for institutions wanting customizable, on‑prem or hybrid model bases. Monitaur represents specialized governance and compliance tooling that centralizes policy, monitoring and vendor validation—critical for meeting regulatory audits and risk controls. Infrastructure and orchestration vendors (e.g., Xilos) and hybrid human+AI service models (e.g., Crescendo.ai) support agentic workflows, contact‑center integration and operational visibility. Selecting platforms requires balancing clinical validation, data governance, explainability and deployment constraints (cloud vs edge). As regulatory scrutiny, multimodal models and edge imaging deployments accelerate, healthcare organizations must combine robust MLOps, domain‑specific assistants, edge inference stacks and governance tooling to deliver safe, auditable clinical AI at scale.