VIDEO

Video has become a critical data source in healthcare, capturing surgical procedures, clinical sessions, and endoscopic recordings. Seen Labs provides structured, clinically focused video annotation services at scale to support diagnostic models, training applications, and care delivery optimization.

Structured Video for Clinical AI

Healthcare organizations are generating large volumes of video—from operating rooms, exam rooms, and procedural suites. Annotating this data is essential for extracting insights, training AI systems, and ensuring quality in both real-time and retrospective analysis. Seen Labs enables teams to build structured datasets that support regulatory-grade models and clinical innovation.

Use Cases Across Medical Video Modalities

We annotate various types of medical video, from invasive surgical footage to outpatient behavioral health encounters. Our service ensures clear, timestamped labeling of relevant clinical content.

• Surgical video: Identify tools, organs, and lesions; track procedural phases and intraoperative communication  
• Endoscopic video: Label anatomical landmarks, polyps, inflammation, and findings throughout the scope path  
• Clinical sessions: Tag verbal and nonverbal cues, emotion, tone, speaker roles, and patient-provider interactions 

Optimized for Complex Video Environments

Medical video brings unique challenges—moving perspectives, obstructed views, varying quality. Our team works with task-specific guidelines to annotate accurately even in difficult recording conditions.

• Frame-level tagging of instruments, anatomical regions, and actions  
• Speaker identification and emotion/intent classification in counseling sessions  
• Region-of-interest and scene segmentation across surgical or diagnostic workflows

Built for Medical Context and Review

Video is inherently complex—every frame tells a story. Our platform is designed for scalable, high-accuracy labeling with advanced segmentation, multi-label classification, and speaker-specific emotion analysis.

• Frame-by-frame object and gesture tagging  
• Multi-speaker emotion, tone, and sentiment labeling  
• Region of interest segmentation and custom tagging logic 

Built for Trust in Care Settings

Seen Labs uses medically trained annotators and clinical QA reviewers to ensure all labels align with real-world clinical practice. Our workflows are tailored for data intended for use in training diagnostic or assistive models.

Multi-Modal and Scalable

We support multi-modal projects that connect video with synchronized sensor data (e.g. vitals), transcripts, audio, or device outputs—providing complete datasets for AI development or analysis.

Supporting Clinical Impact and Research

From robotic surgery support to behavioral health analysis and post-procedure review, our video annotation services help healthcare organizations and AI developers build solutions that interpret real-world care delivery with clinical accuracy.