Clinical Data & AI Solutions
Building high-quality clinical datasets for the next generation of healthcare AI
Clinically Meaningful Data for Healthcare AI
Healthcare AI depends on more than large volumes of data. It requires relevant, well-structured, clinically meaningful, and quality-controlled data.
At Vivoclin, we support healthcare AI companies, technology companies, research organizations, and life-science teams with clinical data sourcing, curation, annotation, quality control, and clinical validation — helping transform complex healthcare information into AI-ready datasets and clinically validated resources.
Discuss Your Data Requirements
From Clinical Data to AI-Ready Datasets
Developing healthcare AI often requires specialized clinical data that is difficult to source, structure, and accurately label. Vivoclin brings together clinical expertise, data operations, and quality-focused workflows to support organizations throughout the data lifecycle.
Clinical Data Sourcing
Identification and sourcing of relevant clinical datasets through appropriate healthcare and clinical networks.
Data Curation & Structuring
Organizing heterogeneous clinical information into structured datasets aligned with specific AI development requirements.
Clinical Annotation
Expert-led annotation and labelling of clinical data using predefined protocols, ontologies, and annotation guidelines.
Data De-identification
Supporting appropriate de-identification and privacy-focused data handling workflows for permitted use cases.
Quality Control & Review
Multi-level review and quality-control processes to improve consistency, accuracy, and reliability of annotated datasets.
Clinical Validation
Clinician-led review and validation of datasets, outputs, and AI models against defined clinical criteria.
Clinical Data We Support
We can support a range of clinical data modalities depending on project requirements, availability, and permitted use.
Medical Imaging
- •X-ray
- •CT
- •MRI
- •Ultrasound
- •Other diagnostic imaging modalities
Cardiology
- •ECG
- •Echocardiography
- •CathLab data
- •Cardiovascular diagnostic information
Clinical Data
- •Electronic health record-derived data
- •Clinical records
- •Laboratory data
- •Diagnostic information
- •Structured and semi-structured clinical datasets
Clinical & Medical Text
- •Clinical notes
- •Medical reports
- •Discharge summaries
- •Medical literature
- •Healthcare documentation
- •Clinical NLP datasets
Built for Healthcare AI
Our workflows can support different stages of AI development.
AI Training
Develop datasets with clinically meaningful labels and annotations for supervised and multimodal AI development.
AI Validation
Create independent, clinically reviewed datasets for evaluating model performance and robustness.
AI Evaluation
Support structured clinical assessment of AI-generated outputs against predefined criteria.
Research & Development
Support academic, healthcare, life-science, and technology organizations developing new clinical AI applications.
Clinical Expertise Meets Data Operations
Generic data annotation is not enough for healthcare. Clinical datasets often require an understanding of medical terminology, diagnostic context, clinical workflows, and specialty-specific interpretation. Vivoclin combines clinical expertise with structured data workflows to help organizations address this challenge.
Clinical experts
Defined annotation protocols
Structured data workflows
Quality control
Clinical review & adjudication
AI-ready dataset
Flexible Engagement Models
We can support projects from initial feasibility through large-scale data programs.
Pilot Projects
Start with a defined sample dataset to evaluate feasibility, annotation requirements, quality, and turnaround time.
Dataset Development
Develop larger clinical datasets according to your specifications, including sourcing, annotation, curation, and QC.
Ongoing Annotation Programs
Support continuous data annotation and clinical review requirements as your AI program scales.
Clinical Validation Projects
Develop and execute structured clinical validation workflows for healthcare AI models and datasets.
Designed Around Your Requirements
Every AI development program has different data requirements. We can work with your team to define:
Whether you need a small validation dataset or a scalable clinical data program, we can structure the engagement around your requirements.
Why Work With Vivoclin?
Clinical Understanding
Our workflows are designed around real clinical context rather than generic data processing.
Flexible & Scalable
Engagements can start with a focused pilot and scale based on project requirements.
Quality-Focused
Structured annotation guidelines, review processes, and QC help maintain dataset consistency.
Multi-Specialty Capability
We can support projects spanning multiple clinical specialties and data modalities.
End-to-End Support
From sourcing and curation to annotation, QC, and clinical validation, we can support multiple stages of the data lifecycle.
Looking for Clinical Data or AI Support?
Tell us what you are building and what your data requirements look like. Our team can work with you to assess feasibility, data requirements, annotation scope, and an appropriate engagement model.
Let's Build Better Healthcare AI.
All data-related activities are subject to applicable laws, regulations, permissions, contractual requirements, and data-use rights. Data availability and project feasibility are assessed on a project-specific basis.