AI Medical Imaging and Speech Platforms for Healthcare covers the convergence of edge vision, automated documentation, and advanced speech tools used across clinical and administrative workflows. By 2026, providers are deploying on-device and cloud-assisted imaging models for faster interpretation and lower latency, while generative and transcriptional AI streamline notes, consent capture, and handoffs. Key categories include Edge AI Vision Platforms for near-real‑time image inference at point of care; Clinical Documentation Tools that synthesize audio, video, and PDFs into actionable records; Voice Synthesis and Transcription for patient communication and virtual agents; and Text-to-Speech tools for readable, accessible patient instructions. Representative tools illustrate common capabilities and trade-offs: OpenCall AI offers HIPAA‑compliant voice agents and messaging automation for appointment booking and patient outreach; Observe.AI focuses on enterprise conversation intelligence and real‑time agent assist for contact centers and clinical call teams; Recall.ai provides APIs/SDKs to capture, transcribe, and index meetings and clinical encounters across platforms; ElevenLabs delivers production-grade TTS, high-fidelity voice cloning, and speech-to-text for patient-facing audio and clinician assistants; PDF.ai turns static clinical documents into conversational knowledge for faster retrieval. Current priorities include integration with EHRs, provenance and audit trails, evaluation against clinical endpoints, bias mitigation, consent/voice-authentication controls, and HIPAA-compliant deployment patterns. Organizations should balance latency and privacy (edge vs. cloud), validate models in clinical settings, and maintain human oversight for diagnosis and documentation. This topic helps buyers and implementers compare platforms by capability, data governance, and workflow fit rather than vendor claims.