Medical imaging and diagnostics now sit at the intersection of specialized imaging suites (eg, DeepHealth-style vendors) and general-purpose AI platforms that provide model backends, orchestration and deployment. This topic covers how Edge AI Vision Platforms, AI Data Platforms and AI Tool Marketplaces are being used to build, validate and deliver clinically useful imaging applications. Relevance (2026): clinical demand for faster, explainable, and locally deployable inference has increased alongside tighter regulatory expectations (device validation, traceability, and data governance). Hospitals and vendors prioritize edge inference for low-latency reads and privacy-preserving workflows, robust data platforms for annotation and federated training, and marketplaces for vetted models and dataset access. Key tools and roles: multimodal model families and cloud AI services (Google Gemini via Vertex AI) provide image–text fusion and scalable inference; conversational and analytic assistants (Anthropic’s Claude family) support report drafting, triage workflows and research analysis; enterprise assistants and governance suites (IBM watsonx Assistant) help operationalize approved models into clinician-facing agents and orchestrations; developer frameworks (LangChain) enable reproducible pipelines and agent-based orchestration for image processing, routing, and multimodal decision logic. Productivity and documentation tools (Notion, PDF.ai) are useful for protocol capture, regulatory submission drafts and querying study reports. Practical considerations: integrate with PACS, ensure model explainability, maintain audit trails, validate across representative cohorts, and plan for on-prem/edge deployment. Evaluations should compare clinical validation evidence, deployment flexibility, governance controls, and marketplace provenance rather than vendor marketing claims.