Clinical AI decision‑support platforms help healthcare professionals synthesize patient data, surface evidence‑based recommendations, and streamline documentation at the point of care. As of 2025, adoption is driven by tighter EHR integration (FHIR/SMART), demands for real‑world validation, and regulatory and payer scrutiny that emphasize explainability, monitoring, and data governance. Key product categories include Clinical Documentation Tools (structured notes and problem lists integrated into workflows), AI Data Platforms (secure ingestion, de‑identification and federated learning for multi‑site models), Data Analytics Tools (interactive BI, cohort analysis and outcomes reporting), and AI Governance Tools (model registries, monitoring, bias assessment and audit trails). Representative technologies illustrate how these pieces fit together: IBM watsonx Assistant enables embedded enterprise virtual agents and clinician‑facing assistants built with enterprise LLMs; LangChain supplies engineering frameworks for building, testing and deploying reliable agentic LLM applications and stateful pipelines; Sisense provides embedded BI and analytics to surface population‑level insights and clinician dashboards; and MindStudio offers no‑/low‑code visual development for prototyping and operating AI agents under enterprise controls. Current trends to watch include stronger clinical evaluation (prospective studies and impact on outcomes), system‑level interoperability, on‑premise or hybrid inference to reduce privacy risk, continuous post‑deployment monitoring for drift and safety, and tighter governance workflows to document model lineage and decisions. Together these components aim to make decision‑support useful, auditable and maintainable in everyday clinical care without replacing clinician judgment.