AI-Powered CT Scan Annotation

30,000+ CT scans annotated at 98.9% segmentation accuracy for AI-driven radiology models. A US-based MedTech AI company developing radiology models for tumour detection and analysis required clinically precise annotation support to accelerate model training and validation. Existing workflows faced rising costs, limited access to qualified medical annotators and growing compliance pressure around handling sensitive patient imaging data.

HIPAA-compliant medical annotation workflows improved radiology AI accuracy, accelerated tumour detection model training and reduced operational costs significantly.

Industry: Healthcare
Service: Generative AI, Intelligent Document Processing, Workflow Automation

30K+: CT scans annotated
98.9%: Tumour segmentation accuracy
−65%: Cost vs US-based annotation teams

Challenge

The client’s AI systems depended on highly accurate tumour segmentation and medical image labeling across large CT and MRI datasets. Generic annotation vendors lacked clinical understanding, while hiring radiology specialists locally proved expensive and difficult to scale. Strict HIPAA and PHI compliance requirements also increased operational complexity across annotation workflows and data handling environments.

Approach

SBL Infotech built a medically trained annotation workforce comprising radiologists, imaging specialists and life sciences professionals experienced in clinical data interpretation. The engagement supported tumour and organ segmentation, contour tracing and high-precision annotation workflows optimized specifically for radiology AI training models. A multi-level quality assurance framework — spanning annotators, QA leads and radiologists — maintained clinical consistency and near-zero rework across all deliverables. HIPAA-compliant workflows, secure annotation environments and full PHI protection protocols ensured operational compliance throughout the engagement.

Outcome

The client established a scalable medical annotation pipeline capable of supporting faster AI model development and validation without compromising clinical accuracy or compliance standards. Improved annotation quality accelerated training efficiency and reduced turnaround cycles across radiology datasets. The engagement significantly lowered operational costs, reduced dependency on expensive in-house specialists and created a long-term annotation framework ready to support future growth in AI-driven diagnostic and medical imaging systems.

IX Case studies

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