AI and Structured Data Solutions
Clinical interpretation that makes imaging data AI-ready
Scribe supports AI and data teams with expert interpretation that converts complex clinical information into structured and annotated datasets.
The reality
AI systems depend on consistent and trusted clinical interpretation
Clinical data is complex, variable, and context driven. Without expert interpretation, structured outputs risk inconsistency, bias, and have limited reliability.
01
High variability in clinical interpretation across datasets
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Limited access to licensed and board-certified experts at the scale required by data programs
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Difficulty translating unstructured clinical information into consistent formats
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Risk of unreliable model outputs without expert-grounded data
What we provide
Expert interpretation for trustworthy AI development in clinical imaging
Our teams of licensed, board-certified pathologists, radiologists, ophthalmologists, and gastroenterologists integrate directly into your data workflows to deliver consistent clinical interpretation that supports reliable AI training, validation, and deployment.
Digital Image-Based Support
Quality assurance and metadata structuring for imaging workflows and spatial biology programs.
Quality check digital images such as scanned pathology slides, colonoscopy videos, or radiology or retinal scans, and identify scanning artifacts
Derive and structure metadata according to FAIR principles (findable, accessible, interoperable, and reusable)
Recommend rescans when necessary to ensure data integrity
Physician-led Annotation & AI Validation Support
Expert-validated ground truth data and AI performance validation to strengthen model reliability.
Create accurate and consistent ground truth annotations
Assist with AI validation datasets for performance studies
Curate training datasets for algorithms with clinical oversight
Review AI outputs and flag performance issues for model refinement
Use cases
Support data driven programs where trust matters most
AI model training and validation
Provide physician-interpreted and annotated data that strengthens model performance and reliability.
Clinical data structuring
Transform complex source data into consistent and analyzable formats.
Human review for AI outputs
Add clinical oversight to validate and refine automated results.
Long running data programs
Sustain expert interpretation across long term data initiatives.
The operating model
Built to integrate into data workflows
Scribe operates within existing data pipelines and processes while aligning with program standards and quality expectations.
Define data scope and interpretation standards
Align on clinical context, definitions, and output requirements.
Embed into data pipelines
Operate smoothly within existing data systems and pipelines.
Deliver expert interpretation at scale
Licensed and board-certified experts support large, ongoing data programs with consistent, reliable outputs.
Quality review and feedback loops
Continuous review ensures alignment, reliability, and trust over time.
Why Scribe
Build AI on expert-validated clinical data
Scribe reduces interpretation variability and strengthens model reliability by grounding structured datasets in clinical expertise.
Physician-validated annotations provide the clinical accuracy AI models need for reliable training and performance.
Clinical data organized to be findable, accessible, interoperable, and reusable across programs and platforms.
Standardized physician review minimizes bias and inconsistency that can compromise model outputs.
Physician review of AI outputs at volume to identify performance gaps and refine model performance.
Scale physician-led interpretation support across datasets and programs as needs evolve.