Clinical Data Engine
Nimblemind offers a faster and safer way to structure, label, and manage multimodal health data with automation, audit trails, and APIs.
Built for healthcare. Validated in the field. Trusted by teams where performance, privacy, and outcomes matter.
Evidence-linked extraction from pathology reports
In a clinical pilot, Nimblemind converted complex gastric-biopsy reports into structured, clinician-reviewable findings with source-text evidence.
98.6% accuracy in clinical case finding
Across an array of de-identified gastric-biopsy reports, Nimblemind correctly extracted 213 of 216 H. pylori-related clinical features, without training on the institution's reports.
Audit-ready outputs by default
Every extracted result is linked to the supporting source text, enabling rapid clinician verification while preserving traceability and reviewability.
From manual review to actionable cohorts
Nimblemind helps teams identify biopsy-confirmed H. pylori cases for treatment follow-up, clinical audit, research cohort assembly, and quality-improvement workflows.
Nimblemind automates ingestion, labeling, governance, and data sharing so teams can go from raw data to AI-ready corpuses in hours instead of months.
Have all your data in one place
Bring together EMRs, imaging, wearables, surveys, and more without relying on custom pipelines or manual formatting.
Automate structuring and labeling
Convert raw data into labeled corpuses with built-in specialty models. Low-confidence results are flagged for review.
Control access with governance
Grant and revoke access by user, team, or study. Every action is tracked with full auditability and time-limited permissions.
Integrate into existing pipelines
Send clean, structured data to notebooks, dashboards, or model training environments without rebuilding.
Multimodal Ingestion
Replaces tools that only support a narrow range of data types
Centralizes data from EMRs, imaging, wearables, and more
Avoids one-off scripts or fragile data pulls
AI Automated Labeling and Structuring
Understand how your AI works with explainability metrics
Domain-tuned models trained on real clinical inputs
Reduces annotation time using built-in automation
Access Control and Logging
Set up time-bound and revocable access controls
Assign user-level permissions by study or role
Monitor every touchpoint with audit logs
IRB-Aligned Governance
Grant and revoke dataset access for fixed durations
Align with institutional requirements and audit prep
Flexible API Integration
Send data to notebooks, dashboards, or cloud environments
Avoid the need for internal pipeline maintenance
Export static files with live access
