Verified professionals
Every contributor is credentialed, identity verified, and matched to projects based on their medical specialty and expertise.
The verified clinician layer for medical AI.
Your model is only as good as the people who labeled its data, and most teams can't prove who that was. Mango puts licensed, verified clinicians behind every label and keeps proof of who did the work.
Radiology +Pathology +Oncology +Cardiology +Dermatology +Neurology +Ophthalmology +Genomics +Radiology +Pathology +Oncology +Cardiology +Dermatology +Neurology +Ophthalmology +Genomics +
About Mango
Mango is the infrastructure for medical data annotation, putting verified medical professionals on your labeling and delivering it with a complete record of who did the work.
Every annotation is completed by qualified experts, reviewed through rigorous quality workflows, and backed by Mango Trail™, providing complete transparency from verification to delivery.
From research institutions to enterprise AI teams, Mango delivers the trusted data behind the next generation of healthcare innovation.
The premise
Every model begins with trustworthy data.
The quality of an AI model is determined long before deployment. It starts with the people who create the data.
Mango ensures every annotation is completed by verified medical professionals and backed by a transparent quality process.
Medical-grade quality
Built-in review workflows, validation checkpoints, and quality controls ensure every dataset meets enterprise standards.
Complete traceability
Every annotation carries a permanent record of who completed it, when it was completed, and how it was reviewed.
+ Mango trail +
Every annotation tells its story.
Most annotation platforms deliver labels. Mango delivers accountability. Introducing Mango Trail™, a permanent, verifiable record attached to every completed annotation.
Know exactly:
- Who completed the work
- Their verified medical specialty
- When it was completed
- How it was reviewed
- Its complete verification history
Mango Trail™ RecordSealed · Immutable
Nodule · 8mmVerifiedSpecialty
Radiology
Confidence score
99.6%
Project
Lung Nodule Detection Dataset
Verified Annotator ✓Radiology specialist
RadiologistIdentity VerifiedCredentials VerifiedSpecialty Verified
Annotation LineageHover a node
+ The platform +
From onboarding qualified specialists to delivering production ready datasets, Mango provides the infrastructure healthcare AI teams need to scale with confidence.
Designed for every stage of medical data annotation.
+ The process +
From expert to dataset.
01Verify
Verify medical professionals.
Credentials, licensure, and identity thoroughly confirmed before any annotation work begins.
02Match
Match experts to projects.
Specialists are aligned to datasets by specialty, expertise, and proven clinical experience.
03Annotate
Annotate with precision.
Purpose-built tools designed around clinical precision, auditability, and diagnostic accuracy.
04Review
Review every submission.
Multi-stage validation and expert consensus keep quality consistently medical-grade.
05Deliver
Deliver trusted datasets.
Get production-ready datasets, with every annotation carrying its permanent Mango Trail™ record.
+ Built on trust +
Trusted by teams building the future of healthcare AI.
Verified
Every contributor is credentialed before joining the platform.
Platform useTraceable
Every annotation carries its own Mango Trail™.
Verified usersReviewed
Every annotation passes through structured quality assurance.
Specialized
Experts are matched to projects based on medical specialty.
Accountable
Know who created every datapoint and how it was reviewed.
Secure
Enterprise-grade identity verification protects every project.
Data types
The clinical data we label.
Verified specialists, matched to the data type they're qualified to read.
Medical imaging
Radiology (X-ray, CT, MRI), read and labeled by clinicians who read scans for a living.
Pathology
Whole-slide images and histology, labeled by pathologists in the right subspecialty.
Clinical text and records
Extraction, coding, and structuring of clinical notes, reports, and EHR data.
Medical language models
Eval, safety review, and preference data for healthcare LLMs, graded against a clinician read.
Genomics and multi-omics
Variant interpretation and multi-omic data, labeled by qualified specialists.
De-identification
PHI detection and redaction, so your data is safe before it's ever labeled.
Built for teams advancing healthcare AI.
AI companies
Train production-ready healthcare models with trusted medical datasets.
Healthcare providers
Accelerate research and clinical innovation with verified annotation workflows.
Research institutions
Create high-quality datasets backed by qualified medical experts.
Medical imaging companies
Scale annotation projects without compromising quality or accuracy.
Pharmaceutical companies
Support drug discovery and clinical research with expertly annotated data.
Enterprise healthcare teams
Manage large-scale annotation operations from one secure platform.
+ Try Mango +
Better healthcare AI starts here.
Build trusted datasets with verified medical professionals, enterprise-grade quality assurance, and complete annotation traceability.
