AUDIO

Medical audio is an essential data source in healthcare. At Seen Labs, we provide high-quality audio annotation services that help healthcare AI systems detect, classify, and interpret sounds such as heart murmurs, lung abnormalities, and clinician-patient interactions.

Structuring Audio for Clinical Use

We support teams developing diagnostic, triage, and assistive tools by turning unstructured audio into usable, labeled datasets. Our annotation workflows are designed specifically for healthcare settings and audio modalities.

Use Cases We Support

Seen Labs annotates a wide range of medical audio types, from auscultation recordings to clinical dialogue, ensuring accuracy and consistency across all annotation tasks.

• Heart auscultation: Identify and time cardiac events (e.g. S1, S2, murmurs, rubs, clicks)  
• Lung auscultation: Label breath sounds such as crackles, wheezes, rhonchi, and stridor  
• Arterial and abdominal sounds: Detect bruits, Korotkoff phases, bowel sounds, and rubs  
• Clinical conversations: Tag intonation, symptoms, medications, and emotional cues

Built for Complex Medical Audio

We support spectrogram-based workflows, time-range selection, and review processes that allow for accurate identification of acoustic features in variable-quality recordings.

• Multi-channel spectrogram tagging and review  
• Signal event timing and range-based labeling  
• Contextual metadata included for downstream training 

Designed for Health AI Development

Our annotators are trained in biomedical audio concepts, and our internal QA processes ensure clinical consistency. All data handling is performed in compliance with healthcare privacy standards.

Multimodal Support

Seen Labs enables projects that combine audio with text, video, waveform, or image data—helping clients train models capable of processing real-world multimodal inputs in clinical settings.

• Trusted by leading biotech and AI teams  
• Designed for regulatory-readiness  
• Built on privacy-first infrastructure 

Scalable, Trusted Annotation

We provide large-scale audio annotation for research, diagnostics, and device development. Our structured output integrates easily into AI pipelines and is adaptable to custom formats.