Medical Imaging

AI-powered medical imaging improves diagnostics, workflow efficiency, and patient care outcomes.

Business impact

• Diagnostic Accuracy — AI improves image interpretation leading to more accurate diagnoses
• Operational Efficiency — Automation reduces manual tasks and speeds up imaging workflows
• Time to Market — Faster AI model development shortens deployment of imaging solutions
• Patient Outcomes — Early and precise diagnosis enables timely and personalized treatment
• Cost Reduction — Optimized workflows and reduced errors lower healthcare expenses

Data requirements

• X-ray, CT, MRI, PET scans (Image) — Primary imaging data for AI analysis and diagnosis
• Radiology reports and clinical notes (Text) — Textual data to support AI interpretation and report generation
• Patient demographics and medical history (Structured) — Structured data to contextualize imaging findings
• 3D imaging and DICOM data (Image) — High-resolution imaging formats for detailed analysis and modeling
• Multimodal imaging and genomic data (Image, Text, Numeric) — Combined data sources for comprehensive diagnostic insights

AI methods and techniques

• Predictive AI — Used for detecting abnormalities and predicting patient outcomes from images
• Generative AI — Generates preliminary reports and synthetic images for training and augmentation
• Symbolic AI — Incorporates domain knowledge for interpretable diagnostic reasoning

AI models and model families

GPT-4, GPT-4V, HOPPR foundation model, Healthcare AI models by Microsoft, Simpleware, Custom proprietary deep learning models

Industries

Real-world evidence

19 documented case studies on record.

Companies using this: Advantis Medical Imaging, Ambra Health, Bayer, Corin, HOPPR, Konica Minolta, Mars PETCARE, Mass General Brigham, Nanox, Nicklaus Children Hospital, Osaka Metropolitan University, Paige, Philips, Picture Health, QDI Systems and 4 more.