AI Medical Imaging & Diagnostic Platforms focus on algorithms and systems that analyze radiology and pathology images at scale, with an emphasis on breast cancer screening and extension to other diagnostic tasks. This topic covers edge AI vision platforms that enable low-latency on‑device inference, cloud MLOps for training/validation/deployment, multimodal LLMs for report generation and retrieval, and clinical assistant orchestration for workflow integration. As of 2026, relevance is driven by higher volumes of screening, increasing regulatory clarity for AI devices, and operational pressure on radiology services to reduce turnaround times and false positives. Key trends include deployment at the edge to preserve latency and privacy, continuous model monitoring and validation via managed MLOps, federated or privacy-preserving training, and using multimodal LLMs to synthesize images plus clinical context into interpretable outputs. Representative tools and roles: Vertex AI — end-to-end managed platform for model discovery, training, evaluation, deployment and monitoring; Google Gemini — multimodal generative models used for contextualizing image findings, creating draft reports, and supporting visual question answering; Cohere — enterprise LLM services for embeddings, retrieval-augmented search, and private text pipelines that can power similarity search and knowledge retrieval from imaging archives; IBM watsonx Assistant — virtual agents and orchestrations that automate clinician interactions, triage results, and integrate AI outputs into EHR/PACS workflows. Together, these technologies enable pipelines where edge vision models screen images, cloud MLOps manages lifecycle and regulatory audit trails, and LLM-powered assistants generate interpretable reports and clinician-facing summaries. No external articles were provided; this overview synthesizes tool descriptions and prevailing industry trends to give a practical, non‑promotional snapshot for decision makers evaluating AI imaging platforms.