This topic covers AI platforms and stacks used to develop, deploy, and operate medical‑imaging and clinical decision‑support systems — from on‑device vision inference to enterprise data platforms that manage imaging, EHR and model lifecycle. Adoption has accelerated through 2025 as multimodal models, stricter regulatory expectations (validation, explainability), and demand for low‑latency, privacy‑preserving inference push work toward two complementary categories: Edge AI Vision Platforms (on‑device or near‑device image preprocessing and inference for modalities such as X‑ray, CT, and ultrasound) and AI Data Platforms (annotation, federated training, compliance, model ops and audit trails). Representative offerings include vendor solutions like DeepHealth, Medscape and Drive Health — which pair image analysis, clinical knowledge and workflow integration — and enabling tools used to build and operate these systems. IBM watsonx Assistant can orchestrate clinician-facing virtual assistants and automate decision workflows; LangChain and MindStudio provide engineering frameworks and low‑code/no‑code environments to prototype and deploy agentic multimodal apps; Replit accelerates developer iteration and lightweight hosting; Microsoft 365 Copilot supports documentation, reporting and synthesis of findings for care teams. Platforms such as Agentverse illustrate emerging marketplaces and orchestration layers for autonomous agents and monitoring, though integration and governance remain nascent. Key trends: tighter integration of DICOM/FHIR pipelines, on‑device and hybrid cloud inference to reduce latency and protect PHI, federated and synthetic data strategies to broaden training datasets, and expanded MLOps for continuous validation and regulatory compliance. Evaluations should therefore weigh accuracy and clinical validation, latency, data governance, interoperability, and operational controls rather than pure model capability.