This topic surveys the AI platforms and tool categories most relevant to diagnostic and clinical decision support as of 2026‑01‑24. It focuses on systems that ingest clinical data, surface evidence, and assist clinicians at the point of care: multimodal foundation models and agent frameworks, edge vision for imaging, curated data pipelines, analytics stacks, and clinical documentation/interaction tools. Recent trends include the integration of agentic LLM frameworks into workflows (e.g., LangChain for building, testing, and deploying reliable LLM agents), wider adoption of multimodal models as backends (Google Gemini and Claude families) to combine text, image and structured data, and stronger emphasis on curated training data (DatologyAI) and enterprise knowledge ingestion (Dataisland, PDF.ai, ChatPDF) to make assistants domain-specific and auditable. Edge AI vision platforms are increasingly used for low-latency image analysis in radiology and point‑of‑care devices, while AI data platforms and analytics tools enable model monitoring, validation, and EHR integration. Conversation- and contact‑center platforms like Observe.AI demonstrate how voice and chat AI can be applied to patient triage, follow‑up and operational workflows. Together these categories address key clinical needs—diagnostic accuracy, workflow efficiency, documentation burden reduction, and explainability—while encountering growing regulatory and privacy expectations. Evaluations should therefore weigh model capabilities, data provenance and curation, deployment environment (cloud vs edge), EHR integration, auditability, and vendor support for clinical validation. This overview helps clinicians, informaticians, and procurement teams compare platforms by function and risk profile rather than marketing claims, enabling informed selection for diagnostic and decision‑support use cases.