Medical Imaging

Use AI to improve medical image analysis, diagnostics, and clinical workflow efficiency

Business impact

• Diagnostic accuracy — AI improves precision in detecting abnormalities and diseases from images
• Time to diagnosis — Automated analysis reduces delays in interpreting medical images
• Operational efficiency — Streamlined workflows reduce manual tasks and increase throughput
• Patient outcomes — Faster and more accurate diagnoses lead to improved treatment success
• Research productivity — Enhanced data analysis accelerates clinical trials and medical research

Data requirements

• MRI scans (Image) — Provide high-resolution images for AI-based analysis and diagnosis
• CT scans (Image) — Offer detailed cross-sectional images used for detecting abnormalities
• X-ray images (Image) — Used for bone and chest imaging, feeding AI detection models
• PET scans (Image) — Supply metabolic activity data to enhance disease detection
• Radiology reports (Text) — Textual data used for training AI to generate and summarize findings
• Clinical trial data (Structured) — Structured data supporting AI model validation and research

AI methods and techniques

• Predictive AI — Used to identify disease patterns and predict patient outcomes from images
• Generative AI — Generates preliminary reports and simulates imaging scenarios for training
• Symbolic AI — Incorporates medical knowledge rules to enhance interpretability and decision support

AI models and model families

GPT-4, GPT-4V, HOPPR foundation model, Healthcare AI models by Microsoft, Simpleware

Industries

Real-world evidence

20 documented case studies on record.

Companies using this: Advantis Medical Imaging, Ambra Health, Bayer, Corin, HOPPR, Imagion Biosystems, Mars PETCARE, Mass General Brigham, Nicklaus Children Hospital, Osaka Metropolitan University, Paige, Peking University, Philips, Picture Health, QDI Systems and 5 more.