CORTISCOPE AI
CORTISCOPE Ai develops Biologically Aware Medical AI models for medical imaging using physics-informed neural networks. Their technology aims to improve and accelerate diagnostic and treatment processes for medical practitioners, even with limited computational resources in remote or field conditions. They address challenges like limited AI access in low-infrastructure settings, the lack of trust in black-box AI, complex and costly AI integration, and the absence of accurate 3D insights for treatment decisions. Their NosoScan technology features a lightweight architecture with less than 30k parameters, data-efficient learning, a hybrid core combining mathematical rules and physics principles for anatomical realism, explainable and compliant AI with built-in transparency, and no GPU dependency, allowing it to run on hospital-grade laptops or embedded devices. Use cases include tumor monitoring in oncology, clinical trials and research, pre-surgical planning/navigation, and resource-limited/field deployments.
CORTISCOPE AI
CORTISCOPE Ai develops Biologically Aware Medical AI models for medical imaging using physics-informed neural networks. Their technology aims to improve and accelerate diagnostic and treatment processes for medical practitioners, even with limited computational resources in remote or field conditions. They address challenges like limited AI access in low-infrastructure settings, the lack of trust in black-box AI, complex and costly AI integration, and the absence of accurate 3D insights for treatment decisions. Their NosoScan technology features a lightweight architecture with less than 30k parameters, data-efficient learning, a hybrid core combining mathematical rules and physics principles for anatomical realism, explainable and compliant AI with built-in transparency, and no GPU dependency, allowing it to run on hospital-grade laptops or embedded devices. Use cases include tumor monitoring in oncology, clinical trials and research, pre-surgical planning/navigation, and resource-limited/field deployments.