This topic covers the leading enterprise AI platforms used in healthcare: systems that ingest clinical data, automate documentation, support patient and clinician conversations, and enforce security and regulatory controls. Demand for these platforms has grown as health systems adopt multimodal large language models, conversational agents, and voice AI to reduce clinician burden, accelerate coding and documentation, and triage routine patient interactions while protecting PHI. Key categories include AI Data Platforms (centralized data lakes, feature stores, and model-hosting for EHR-integrated workflows); Clinical Documentation Tools (LLM-assisted note generation, coding, and summarization); AI Security & Governance (model monitoring, lineage, access controls, and bias detection); and Regulatory Compliance Tools (audit trails, consent management, and controls for HIPAA/GDPR/EU AI Act compliance). Representative tools illustrate these roles: Google Gemini and Anthropic’s Claude provide multimodal and conversational LLM services for clinical summarization and decision support; IBM watsonx Assistant enables enterprise virtual agents and no-code or developer-driven assistants for patient-facing bots and clinician copilots; PolyAI and Observe.AI specialize in voice-first and contact-center AI for multilingual patient engagement and real-time agent assist. LangChain and similar engineering frameworks support building, testing, and orchestrating reliable agentic applications and integrations with EHRs and governance layers. As of 2026, healthcare deployments emphasize provable privacy, explainability, continuous monitoring, and tight integration with clinical workflows. Organizations evaluating platforms should weigh clinical accuracy, deployment model (private cloud/on‑prem), interoperability, and the vendor’s capabilities for security, auditability, and regulatory readiness.