Medical imaging AI platforms for cancer screening and diagnostics cover the toolchains and operational stacks used to build, validate, deploy and monitor image‑based models in clinical settings. This topic spans two core categories — Edge AI Vision Platforms (for optimized on‑device inference and latency‑sensitive point‑of‑care use) and AI Data Platforms (for curated imaging datasets, annotation, training pipelines, and governance). Contemporary implementations combine model optimization and hardware support (noting industry consolidation such as NVIDIA’s 2024 acquisition of Deci) with developer and orchestration tooling that accelerates clinical workflows. Engineering frameworks like LangChain and agent development environments such as Warp are repurposed to orchestrate multi‑step workflows (image triage, report drafting, cross‑referenced evidence retrieval). Enterprise assistants (IBM watsonx Assistant) can automate structured reporting and user interactions, while answer engines (Perplexity AI) help surface sourced literature or guidelines as part of decision support. Developer platforms (GPTConsole) and code assistants (Tabnine) improve reproducibility, governance, and secure deployment pipelines for regulated settings. Key trends as of early 2026 include increased deployment of optimized models at the edge for faster screening, stronger emphasis on clinical validation and explainability, and tighter data governance and privacy controls (including federated and hybrid training patterns). Effective platforms therefore integrate model optimization, provenance and auditing, clinical validation workflows, and deployment tooling that aligns with regulatory requirements and hospital IT environments. Evaluations should prioritize interoperability with PACS, real‑world performance monitoring, and mechanisms for traceable updates and clinician oversight rather than vendor claims.