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Arterys AI

Arterys AI is an AI-powered Healthcare tool — AI medical imaging analysis for radiology. Best for: Medical Research & Health Monitoring. Pricing: Paid (GateOnAI Score: 46/100).

AI medical imaging analysis for radiology

PaidVerifiedMedical ResearchHealth MonitoringMental HealthPatient Care

Provides AI-powered analysis of medical images, delivering automated segmentation and diagnostic metrics across CT, MRI, and ultrasound scans. The platform generates volumetric measurements of lesions, calculates perfusion parameters, and highlights abnormal tissue with color overlays. A built-in quality‑control dashboard flags low‑confidence results for radiologist review. Users can select pre‑trained models for lung nodule detection, cardiac function assessment, or liver tumor quantification, and fine‑tune them with local data via a simple web interface.\nArterys runs entirely in the cloud, so hospitals install no on‑premise hardware and can scale compute resources per study volume. The service integrates with existing PACS and RIS via DICOMweb, allowing seamless pull‑and‑push of images and reports. Real‑time collaboration lets multiple radiologists annotate the same case, with changes synced instantly. All data transmission meets HIPAA and GDPR standards, and the core algorithms hold FDA 510(k) clearance for several indications.\nThe solution targets radiology departments, imaging research groups, and teleradiology providers who need consistent quantitative readouts and faster turnaround. By automating routine measurements, clinicians can focus on interpretation and patient communication, reducing report turnaround by up to 30% in pilot studies. Pricing follows a subscription model based on scanned volume, with enterprise contracts that include dedicated support and model updates; there is no free tier. Compared with legacy manual tools, Arterys offers higher reproducibility and immediate cloud access, though institutions must maintain reliable internet connectivity. Early adopters report smoother multi‑center trials because the platform stores de‑identified datasets centrally for joint analysis.

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