License, de-identify, and annotate healthcare data across text, audio, imaging, and multimodal datasets—built for privacy, quality, and scale.
Over 80% of healthcare data is unstructured—spread across clinical notes, EHRs, medical dictations, imaging, and diagnostic reports. This data is powerful, but difficult to access, expensive to prepare, and highly regulated.
AI teams face critical challenges:
Without the right data foundation, even the most advanced algorithms fail to deliver impact.
Shaip solves this problem by putting data first.
Shaip is a trusted healthcare data partner helping organizations build, train, and deploy AI models using ethically sourced, compliant, real-world healthcare data.
Unlike vendors focused only on annotation, Shaip supports the entire healthcare AI data lifecycle:
This unified approach reduces risk, shortens timelines, and ensures your models are trained on data that reflects real clinical complexity.
High-quality, compliant data across text, audio, imaging, and multimodal AI.
Access high-quality, real-world healthcare data—off-the-shelf or custom collected—to match your exact AI requirements.
Capabilities include:
Remove PHI/PII so data can be used safely for AI training and analytics.
Key features:
Turn raw healthcare data into model-ready training datasets with expert labeling and QA.
Annotation workflows include:
Ready-to-use, compliant datasets to accelerate healthcare AI development.
Access a curated catalog of de-identified healthcare datasets across clinical text, EHRs, medical audio, imaging, and multimodal data—available for rapid licensing and immediate AI training.
From clinical text and EHRs to audio, imaging, and synthetic conversations—Shaip enables AI across the healthcare data lifecycle.
Extract diseases, drugs, symptoms, tests, and other clinical entities from unstructured text for AI training and analytics.
De-identify and annotate oncology datasets to accelerate cancer-focused NLP models and clinical research.
Convert unstructured EHRs and clinical notes into structured signals such as conditions, medications, and labs.
Train AI models to review clinical documentation faster and improve approval accuracy and compliance.
Build clinical speech-to-text and documentation pipelines using physician dictation audio and transcripts.
Create labeled imaging datasets for detection, classification, and segmentation to support diagnostic AI.
Combine clinical notes, EHR data, medical audio, and DICOM images to train advanced multimodal AI models.
Generate realistic physician–patient dialogues to train AI models on medical language, context, and conversation flow.
Trusted healthcare data—sourced ethically, de-identified securely, and delivered with expert quality at scale.
From sourcing and licensing to de-identification and labeling—one partner across the healthcare AI data lifecycle.
Expert support across clinical text, EHRs, medical audio, imaging, and multimodal datasets.
Healthcare-trained specialists—not generic crowd workers.
Consent-driven collection with clear data lineage and auditability.
Strong security practices that protect sensitive healthcare data throughout the workflow.
Multi-layer QA and human-in-the-loop validation for consistent, accurate datasets.
Trusted to deliver large, complex healthcare datasets for enterprise AI programs.
HIPAA Safe Harbor, Expert Determination, and GDPR-aligned de-identification by design.
De-identified clinical data prepared at scale to power GenAI models for predictive healthcare insights.
Problem: Needed large, compliant clinical datasets for GenAI training, but data access, quality, and privacy were major blockers.
Solution: Shaip curated and de-identified clinical data with expert validation to ensure accuracy, safety, and model readiness.
Result: Faster GenAI model development with privacy-safe data and reliable predictive insights in a regulated environment.
Synthetic clinical audio + transcripts delivered to train speech models without exposing sensitive real-world recordings.
Problem: Required large volumes of diverse clinical speech data, but privacy constraints and limited availability slowed progress.
Solution: Shaip generated realistic synthetic clinical audio and delivered high-quality transcriptions for training and evaluation.
Result: Accelerated speech AI training with privacy-safe data and improved model performance across clinical language scenarios.
Scale data de-identification across different regulatory jurisdictions, including GDPR, HIPAA, and as per Safe Harbor.
Did you know AI models that merge diverse medical data can enhance predictive accuracy for critical care outcomes by 12% or more over single-modality approaches?
Think about the last time you visited a doctor. Behind every diagnosis, prescription, or recommendation lies data—your vitals, your lab results, your medical history.
Why do we – as a human civilization – need to nurture scientific competencies and foster R&D-driven innovation? Can’t conventional techniques and approaches be followed for eternity?
Empowering teams to build world-leading AI products.
Healthcare AI uses artificial intelligence to improve medical services like diagnosis, treatment, and patient management by analyzing healthcare data.
AI improves diagnosis accuracy, reduces costs, automates tasks, and provides personalized treatments, leading to better patient care and outcomes.
AI is used in medical imaging, disease diagnosis, drug discovery, remote patient monitoring, virtual health assistants, and hospital management.
AI offers personalized treatment plans, early disease detection, and real-time remote monitoring, enabling timely interventions and better outcomes.
Shaip de-identifies sensitive data, removing personal information to comply with regulations like HIPAA and GDPR, ensuring secure and ethical data use.
NLP extracts insights from unstructured medical data like physician notes, identifying symptoms, diseases, and treatments for better decision-making.
Yes, we can customize datasets based on demographics like age, gender, or ethnicity, and geographic regions to match your project’s specific needs.
Delivery timelines depend on the complexity and volume of the data requested. We work efficiently to deliver high-quality data within the agreed time frame.
We offer sample datasets or pilot projects so you can evaluate the quality and relevance of the data before committing to a larger purchase.
Pricing depends on factors like data type, volume, customization, and delivery timeline. Contact us for a detailed quote tailored to your project.
