Accurate, consistent labels on X-rays, scans, slides and clinical text, to improve diagnostic AI and patient outcomes.
Use cases
Training data built around the use cases our clients take to production.
Automated diagnostics
We provide high-quality data annotations that enable AI systems to analyze medical images and detect abnormalities. This allows healthcare professionals to make faster, more informed diagnostic decisions, leading to earlier treatments and improved patient outcomes.
Dentistry with AI
Annotated dental X-rays, intraoral images and 3D scans help AI flag cavities, gum conditions and alignment issues, supporting faster and more consistent diagnosis.
Telemedicine support
Through precise annotations, our data helps power AI-driven telemedicine platforms, ensuring accurate diagnoses and treatment recommendations during virtual consultations. This allows healthcare providers to offer seamless, high-quality care remotely, improving access to health services for all.
Drug discovery
Our expert annotations empower AI to analyze complex biological and chemical data, significantly speeding up the drug discovery process. By improving the accuracy of AI models, we help pharmaceutical companies identify potential treatments faster, reducing time to market and cutting development costs.
Health record management
Our data annotation services help streamline the organization of electronic health records (EHRs), enabling AI to efficiently sort and manage vast amounts of patient data. This ensures healthcare providers can access critical information quickly, improving patient care and operational efficiency.
Annotation types
Pick an annotation type to see what it's for and how it looks on real data.
Orthopedic data annotation focuses on labeling medical imaging like X-rays, MRIs, and CT scans to train AI systems in identifying bone fractures, joint conditions, and musculoskeletal disorders. These annotations help improve diagnostic accuracy and assist orthopedic professionals in clinical decision-making.
What makes it hard
These are the problems that quietly break models in this domain, and how our guidelines handle them.
Edge cases we label
Medical images need reviewers who understand anatomy and pathology, not just the annotation tool.
Borderline cases are escalated and resolved by consensus instead of guessed by one annotator.
Data is handled under strict access control, with de-identified images wherever possible.
Organ and lesion outlines follow agreed protocols so masks are comparable across annotators.
How we work
Every project follows the same five steps, with multi-layer QA backing our 98%+ accuracy guarantee.
We review your data, edge cases and success criteria, then write annotation guidelines with you.
A trained team labels a pilot batch, following the agreed guidelines to the letter.
You review the pilot. We fold your feedback into the guidelines before scaling up.
Every batch passes peer review, expert QA and automated checks before it leaves us.
Labels ship in your format on your schedule, ready to drop into training.
Proof
“Intellisane AI played a key role in helping us reach 97% accuracy in automating foundation layout detection. Their deep understanding of spatial data and labeling precision brought measurable improvements to our AI pipeline. The team was communicative, detail-oriented, and delivered everything ahead of schedule.”
Case studies
See how other teams used our data in production.
Get started
Tell us what your model needs to learn. We'll scope the work and label a free pilot batch.
