Medical Data.Expert Review.AI-Ready Delivery.
OmniSource is building a managed data infrastructure layer for medical AI, multimodal annotation, expert-reviewed QA, and healthcare model evaluation.
Structured Data Workflows for Healthcare AI
Healthcare AI companies do not only need more data. They need the right data, structured properly, reviewed by domain experts, and delivered with clear quality controls.
Medical Imaging Data
DICOM, radiology reports, modality-specific datasets, body-part filtering, and imaging annotation workflows.
Clinical Reasoning QA
Doctor-reviewed reasoning traces, diagnostic QA, triage scenarios, and clinical model evaluation.
Multimodal Data
Image, video, text, audio, biometric, and structured data workflows for AI training and evaluation.
Expert Review & Validation
Human-in-the-loop review, QA sampling, senior review, escalation, and delivery reporting.
Medical AI Data, Built for Real Model Development
OmniSource supports selected medical AI workflows where data quality, expert review, and structured delivery matter more than raw volume.
Radiology AI
DICOM review, imaging annotation, report alignment, body-part classification, and modality-specific datasets.
Clinical Reasoning
Doctor-reviewed clinical questions, diagnostic reasoning, triage evaluation, and medical QA workflows.
Healthcare Model Evaluation
Human review of healthcare AI outputs, safety checks, hallucination detection, and clinical relevance scoring.
Medical Text & Reports
Clinical note review, report structuring, summarization QA, terminology validation, and de-identification-aware processing.
OmniSource supports AI data workflows only. OmniSource does not provide medical diagnosis, treatment, patient care, or clinical decision-making services.
For Healthcare Data Partners
Turn underutilized medical data into structured, privacy-aware AI datasets for research and model development.
Healthcare providers, clinics, imaging centers, labs, and regional data holders may have valuable historical medical data. OmniSource can help structure, de-identify where applicable, annotate, and prepare selected datasets for AI research and development use cases.
Data Value Discovery
Identify valuable datasets across imaging, reports, clinical notes, lab records, and multimodal sources.
De-identification-Aware Processing
Support workflows that remove or redact identifiable fields before external delivery, subject to applicable legal and compliance review.
Structured Dataset Preparation
Convert fragmented medical records and imaging files into searchable, usable, AI-ready datasets.
Revenue / Partnership Model
Explore commercial data partnerships, revenue share, licensing, or project-based data preparation models.
Healthcare Institution
Secure Processing
De-identification
Structuring
AI Research
All data partnerships are subject to compliance review, applicable consent requirements, and local legal frameworks.
For AI and Medical Device Companies
Access medical datasets and expert-reviewed workflows for model training, validation, and evaluation.
Dataset Sourcing
Source datasets by modality, body part, region, patient characteristics, report availability, and annotation needs.
Custom Cohort Building
Build project-specific cohorts for AI training, evaluation, or model validation.
Annotation & Review
Add expert-reviewed labels, report alignment, clinical QA, and reviewer notes.
Delivery Format
Deliver structured datasets, DICOM files, metadata tables, annotation files, QA reports, and review summaries.
Datasets are designed to support model development and R&D workflows. Structured for downstream regulatory preparation if required by client.
From Raw Medical Data to AI-Ready Dataset
A structured workflow for transforming medical data into validated, expert-reviewed datasets.
Data Intake
Receive source data from approved partners or client-provided datasets.
Data Mapping
Identify modality, body part, report availability, metadata fields, quality gaps, and target use case.
De-identification
Remove or redact personal identifiers from text, metadata, and image layers where applicable.
Structuring
Normalize files, metadata, labels, and reports into usable dataset formats.
Expert Annotation
Route tasks to trained medical reviewers, annotators, or QA specialists.
QA Review
Perform sampling, reviewer agreement checks, senior review, and escalation for ambiguous cases.
Delivery
Deliver datasets, annotation files, QA summaries, and documentation in client-ready formats.
Scale Plan
Convert pilot workflow into repeatable data production pipeline.
Data Intake
Receive source data from approved partners or client-provided datasets.
Data Mapping
Identify modality, body part, report availability, metadata fields, quality gaps, and target use case.
De-identification
Remove or redact personal identifiers from text, metadata, and image layers where applicable.
Structuring
Normalize files, metadata, labels, and reports into usable dataset formats.
Expert Annotation
Route tasks to trained medical reviewers, annotators, or QA specialists.
QA Review
Perform sampling, reviewer agreement checks, senior review, and escalation for ambiguous cases.
Delivery
Deliver datasets, annotation files, QA summaries, and documentation in client-ready formats.
Scale Plan
Convert pilot workflow into repeatable data production pipeline.
Expert Review Network
OmniSource is building a selected network of medical reviewers, annotators, and QA leads to support expert-reviewed AI data workflows.
Our pilot network is being developed around medical reviewers, clinical trainees, annotation specialists, and QA leads across selected regions, starting with Pakistan and expanding into additional markets.
Medical Reviewer
Medical QA / Clinical Reasoning / Report Review
Radiology Annotation Reviewer
DICOM Review / Report Alignment / Image QA
Clinical Text Reviewer
Clinical Note Review / Summarization QA / Terminology Validation
Multimodal Annotator
Segmentation / Bounding Box / Video QA
Physical AI Operator
Egocentric Video / Robotics Task Labeling
QA Lead
Sampling / Escalation / Delivery Review
Medical and Multimodal Data Types
Supporting diverse data modalities for healthcare AI and multimodal model development.
Medical Imaging
DICOM, CT, MRI, X-ray, ultrasound, mammography, pathology imaging where available.
Clinical Text
Radiology reports, clinical notes, discharge summaries, diagnostic summaries, and structured medical text.
Medical QA
Clinical reasoning, triage scenarios, diagnostic evaluation, medical question answering, and safety review.
Biometric / KYC
Facial, behavioral, and identity verification datasets where legally permitted and properly governed.
Subject to consent, de-identification, local law, and client-specific governance.
Physical AI
Egocentric video, field task capture, robotics data, spatial reasoning, and industrial workflow data.
Multimodal
Image, video, audio, text, metadata, and structured labels for AI training and evaluation.
Quality Control Is the Product
For medical AI data, the value is not just access. The value is repeatability, traceability, reviewer discipline, and delivery documentation.
Annotation Guidelines
Clear labeling rules, inclusion criteria, exclusion criteria, and edge-case handling.
Reviewer Qualification
Reviewer assignment based on domain background, training, and pilot performance.
Multi-Layer QA
Sampling, reviewer agreement, senior escalation, and consistency checks.
Audit Trail
Task history, reviewer actions, QA results, and delivery documentation.
Data Governance
Consent, de-identification, access control, and lawful-use review where applicable.
Delivery Report
Dataset summary, quality notes, limitations, and recommended next steps.
QA Review Hierarchy
Start With a Controlled Pilot
OmniSource is available for selected pilot projects where clients need expert-reviewed, quality-controlled medical or multimodal AI data workflows.
Pilot Options
Medical Imaging Dataset Pilot
Small controlled dataset with DICOM / report alignment and QA summary.
Clinical Reasoning QA Pilot
Doctor-reviewed QA set for healthcare model evaluation.
Radiology Annotation Pilot
Image annotation, body-part classification, report alignment, and QA review.
Medical Text Review Pilot
Clinical note structuring, summarization QA, terminology validation, and redaction-aware workflow.
Physical AI / Multimodal Pilot
Video, image, real-world task labeling, or robotics-related data workflow.
Pilot Deliverables
- Scoped workflow
- Annotation guideline
- Sample dataset or client-provided dataset review
- Expert review process
- QA summary
- Delivery format recommendation
- Scale-up plan
Request a Pilot
Part of the Leviathan Compute + Data Infrastructure Stack
Leviathan Group builds around power-backed compute infrastructure. OmniSource extends the platform into the data layer: medical AI data, expert-reviewed workflows, multimodal data, and human-in-the-loop QA.
Power
Leviathan secures low-cost renewable power and hosted compute infrastructure.
Compute
Mining and future compute infrastructure create the physical foundation.
Data
OmniSource builds expert-reviewed data workflows for AI model development.
Build Your Medical AI Data Workflow With OmniSource
For medical AI, imaging data, multimodal annotation, expert review, or healthcare data partnership discussions, contact the OmniSource team.