ArtemisAI turns existing hospital cameras into an AI-powered monitoring system that continuously analyzes video and vital‑sign feeds to detect respiratory distress, seizures, falls, agitation, and pain with 99.9% accuracy. The platform delivers real‑time, HIPAA‑compliant alerts to clinicians via API or dashboard without storing video, enabling faster intervention and supporting research with anonymized multimodal datasets.
Funding
$855K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Hospitals rely on intermittent manual checks and dedicated monitoring devices, which can miss early signs of patient deterioration such as respiratory distress, seizures, falls, or pain, especially when staff are stretched thin or patients are in low‑visibility settings.
Solution
ArtemisAI converts any standard camera into an AI‑driven clinical monitoring copilot that continuously analyzes video streams together with vital‑sign feeds. The platform detects respiratory distress, seizure activity, falls, agitation, and pain in real time, generating alerts with 99.9% accuracy. Video is processed on the fly without permanent storage, and all data are encrypted and handled in a HIPAA‑compliant manner. Clinicians receive instant notifications and visualizations of detected events, enabling faster intervention and reducing the risk of missed deterioration. The system also creates high‑volume multimodal datasets for research while preserving patient privacy.
Target Audience
Primary customers are acute care hospitals, intensive care units, and post‑acute facilities that need continuous patient monitoring, as well as clinical research teams seeking high‑resolution multimodal data.
Features
- Real‑time video analysis fused with vital‑sign data for multimodal event detection
- Continuous 24/7 monitoring that flags respiratory distress, seizures, falls, agitation, and pain
- HIPAA‑compliant, end‑to‑end encryption with no persistent video storage
- Easy integration with existing camera hardware; no specialized sensors required
- High‑accuracy alerts (99.9% confidence) delivered to clinical workflows via API or dashboard
- Generates large‑scale, anonymized multimodal datasets for research and model improvement
- 3D spatial tracking of patient movements to enhance fall prevention and activity monitoring