Modality, labeling rules, edge cases and acceptance criteria are defined around your clinical use case.
A full-time medical annotation team completes the work using the agreed protocol—not a rotating freelance pool.
Outputs are reviewed against the protocol, with each delivered batch linked to clear annotation and review records.
Oncology
Cardiology
Surgery
Colonoscopy
Blood, tissue and partial occlusion made surgical tool boundaries difficult to define, while access to surgeon oversight was costly and difficult to scale.
Subul assembled 10 medical image annotators supervised by 5 surgeons, using polygon annotation and multi-level quality control to label 2,365 medical images.
The project reported 99% accuracy in surgical tool recognition.
We align on the modality, annotation guidelines, edge cases and acceptance criteria.
Start with a free 4–6 hour trial batch to validate the workflow and expected quality.
Dedicated annotators complete the work, with clinical specialists supporting quality review.
Receive structured, model-ready outputs and refine the next batches through continuous feedback.
Medical AI requires more than correctly placed labels. Subul combines dedicated annotation teams, clinical input and structured quality checks to maintain consistency from the first trial batch through scaled delivery.
Full-time annotators—not shared freelancer pools.
Structured checks keep every batch consistent.
Medical specialists guide complex cases.
Trained teams grow with your data needs.
Tell us about your data, annotation guidelines and quality requirements. We’ll recommend the right workflow and prepare a free 4–6 hour trial batch after the call.
Clinically reviewed medical training data, delivered by dedicated annotation teams.
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