Services - Data Labelling for Healthcare | Indivillage Tech Solutions

DATA ANNOTATION FOR HEALTHCARE

We deliver expert-grade annotation and precision labelling at scale, so your healthcare AI learns faster, diagnoses better, and delivers results that truly improve patient outcomes.

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Labels that drive better care

From diagnostics and triage to claims automation, clinical AI fails when labelling lacks medical nuance. We combine medically trained annotators with QA workflows tailored to healthcare, annotating complex inputs like scans, transcripts, and care notes with domain-specific accuracy.
Whether you’re tagging chest X-rays, aligning doctor-patient conversations, or classifying treatment intents, our teams understand abbreviations, ambiguity, and edge cases so your models don’t mislearn. The result? Clinically usable data annotated with purpose, at scale.

Annotation That Delivers

Vision Labels

Bounding boxes, polygons, and masks for medical images, from x-rays and MRIs to dermatology photos and device screens.

Text Tags

Extract conditions, intents, dosages, symptoms, and PHI from unstructured medical text, discharge summaries, and EHR notes.

Audio Markup

Tag speaker turns, symptoms, medical cues, and clinical intent from doctor-patient conversations and telehealth transcripts.

Clinical Data, Real Impact

We annotate complex healthcare data types — powering accurate, scalable, and compliant AI for health.

Diagnostic Imaging

Label x-rays, CTs, and other scans with condition markers and regions of interest to support AI-assisted diagnosis.

Care Documentation

Annotate discharge summaries, prescriptions, and patient reports for classification, extraction, and claim validation.

Virtual Health

Tag doctor-patient audio for symptoms, sentiment, and spoken cues to improve virtual assistants and telehealth workflows.

Medical Research

Label journal data, trial logs, and literature for NLP models driving drug discovery and biomedical search.

Using AI to Deliver Safe HIV Testing With 100% Accuracy >631K+ interpretations completed

Training AI to Treat Right

Explore how healthcare annotation powers diagnostics, workflows, and patient-facing tools.

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Frequently Asked Questions

Quick answers to help you make smarter, faster decisions with confidence

What types of annotation do you support?▾

We support image, video, audio, text, and multi-modal annotation, including segmentation, classification, transcription, PHI redaction, and metadata tagging for healthcare.

Can you customize annotation workflows for our use case?▾

Yes. We design custom workflows for radiology, patient records, clinical audio, and more, tailored to your use case, tools, and compliance protocols.

How do you ensure annotation quality and consistency?▾

We use medical QA layers, consensus scoring, reviewer training, and edge case protocols, all monitored by project-level specialists.

Do you support multilingual or regional content?▾

Yes. We annotate in English and multiple Indian languages, accounting for dialect, tone, symptoms phrased in native terms, and healthcare terminology.

Can you integrate with our internal tools?▾

Yes. We support CVAT, Prodigy, Labelbox, and custom EMR/annotation interfaces with secure APIs or in-platform work.

Is the data handled securely and ethically?▾

Always. We follow HIPAA, GDPR, and SOC2-aligned practices. All patient data is consented, anonymized, and processed securely in-house.