Digital Pathology

Whole-slide image analysis automates tissue abnormality detection to speed and improve pathology diagnoses

Business impact

• Diagnosis speed — Shortens time from slide digitization to diagnostic decision-making
• Diagnostic accuracy — Improves correctness and consistency of pathology interpretations
• Workflow efficiency — Streamlines slide handling and reduces manual workload for pathologists
• Collaboration efficiency — Enables remote sharing and joint review of digital slides
• Data privacy — Minimizes physical slide transfers, enhancing patient data security

Data requirements

• Whole-slide digital pathology images (Image) — Primary input for AI models to analyze tissue morphology
• Clinical and pathology reports (Text) — Provide labels and context for supervised AI training
• Patient metadata and electronic health records (Structured) — Support correlation of image findings with clinical outcomes
• Molecular and genomic data (Structured) — Augment image analysis for precision medicine insights
• Pathologist annotations (Text) — Used for model validation and refinement

AI methods and techniques

• Predictive AI — Detects and classifies pathological features to predict disease states
• Generative AI — Synthesizes augmented training data and enhances image quality
• Symbolic AI — Incorporates domain knowledge for explainable diagnostic reasoning

AI models and model families

GPT-4o, Claude, NVIDIA Clara, Foundation vision models, Custom convolutional neural networks

Industries

Real-world evidence

12 documented case studies on record.

Companies using this: Aiosyn, Dana-Farber Cancer Institute, Dell Technologies, LigoLab, Mayo Clinic, NHS, NYU Langone Health, Oxford University Hospitals NHS Foundation Trust, Paige, Siriraj Piyamaharajkarun Hospital, Techcyte, Inc., Veracyte.