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Read MoreHealthcare AI Services by Digital Divide Data (DDD)
Build Safer, Smarter Healthcare AI with Human-in-the-Loop Precision
Healthcare AI succeeds (or fails) on data quality, clinical context, and rigorous validation. Digital Divide Data (DDD) delivers human-in-the-loop data operations and evaluation services that help healthtech teams move from prototypes to production faster, more safely, and at scale.
What we do
DDD supports healthcare and healthtech organizations across the AI lifecycle from dataset creation and annotation to model evaluation and iterative improvement spanning computer vision, NLP, multimodal models, and generative AI.
Our focus areas
- Healthcare training data pipelines for CV, NLP, and multimodal models
- Generative AI data + evaluation (LLM tuning support, RLHF-style feedback, safety testing)
- Model validation workflows with consistent QA and clinician-in-the-loop options (as needed)
Why DDD
- Proven scale (millions of labeled data points; large, trained labeling workforce)
- Human-in-the-loop accuracy for high-stakes healthcare use cases
- Coverage across CV + NLP + GenAI including evaluation and testing workflows
- Secure, enterprise-ready delivery with published security standards
Healthcare AI capabilities
Use Cases
Digital Pathology
DDD’s pathology-trained teams annotate cells and tissues in Whole Slide Images (WSI) to support digital pathology models, disease detection, and pharmaceutical research.
Digital Radiology
DDD annotates X-rays, CTs, MRIs, and ultrasounds at scale, delivering high-quality ground-truth data for clinical diagnostics and radiology AI applications.
Medical Texts
DDD structures clinical notes, medical records, and audio transcripts to power NLP automation, decision-support tools, and intelligent healthcare assistants.
Robotic Surgery
DDD supports surgical AI with precise instrument tracking, lesion detection, and phase identification for robotics, endoscopy, and real-time procedure analytics.
Sports Medicine
DDD provides point tracking and movement annotation for rehabilitation, athlete monitoring, and behavioral health applications to improve safety and performance insights.
What Our Clients Say
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Frequently Asked Questions
Do you have medically trained annotators?
Yes. We employ specialized annotators trained in anatomy, radiology basics, and clinical workflows to deliver precise medical datasets.
Can DDD handle large-scale medical imaging datasets?
Absolutely. Our global workforce supports enterprise-grade image annotation with rapid turnaround times.
How do you ensure quality for diagnostic AI use cases?
We use multi-layer QA, expert reviews, inter-annotator agreement checks, and clinical oversight to ensure accuracy and consistency.
Does DDD support model validation for healthcare AI products?
Yes. We create ground-truth datasets, conduct error analysis, and support V&V workflows required for regulatory pathways.