In medical AI, precision isn't optional, it’s foundational. Creating high-quality training data requires more than just labeling tools; it takes a unified system that brings annotation, validation, prediction, and compliance together into a single, intelligent workflow.

That’s where Gesund.ai comes in.

We’re not just another annotation tool. Gesund.ai is the AI infrastructure behind real-world clinical applications—built from the ground up for radiology, pathology, and multi-modal healthcare data. With native support for 2D, 3D, and microscopy annotation, our platform is designed for workflows that are accurate, explainable, and fully auditable.

Unlike many platforms that focus only on labeling, Gesund.ai combines medical focus, integrated validation, prediction support, and regulatory readiness—all in one. Today, we will focus on our Annotation Module.

Creating a Medical AI Annotation Project

Every workflow begins with structured project creation. Gesund.ai guides you through setup with inputs like:

• Project Type (2D, 3D, Microscopy)

• Imaging Modality (CT, MRI, Histopathology, etc.)

• Assignment Mode (manual or rule-based)

• Role definitions (Annotator, Reviewer, Viewer)

You can also import datasets via CSV or S3, connect to prediction models, and define class hierarchies and label sets.

Gesund.ai supports scalable, multi-modality studies with role-based views and both cloud and on-prem deployment options. Perfect for teams in hospitals, labs, and regulatory environments.

Tracking Annotation Progress with Full Transparency

The Annotation Dashboard provides visibility into:

• Study assignments

• Annotation completion by user

• Segmentation previews

• Audit trail of every action

User-based permissions let each stakeholder focus only on what matters to them, while project managers retain full control. This layered access model simplifies collaboration and ensures quality without confusion.

A Unified Viewer for All Data Type

1. Microscopy Annotation

Optimized for whole-slide images (WSI), Gesund.ai ’s viewer allows:

• Smooth navigation of ultra-high-resolution slides

• Region-based labeling of cell types

• Custom colormaps, measurement overlays

• Multi-object editing, label-level stats, audit trails

This is a microscopy-native environment, not a generic tool retrofitted for histopathology

2. 3D Medical Imaging Viewer

For CT, MRI, PET and volumetric segmentation, the 3D viewer offers:

• Keyboard or scroll-based slice navigation

• Smart tools (region-growing, auto-propagation)

• Lock/hide per class for multi-organ annotation

• Real-time study tracking

Built to scale across the full human anatomy with AI-assisted and manual tools in the same interface.

3. 2D Image Annotation

Perfect for high-throughput tasks like X-rays and axial CT slices:

• Bounding boxes and masks

• Per-class toggle and custom colors

• Prediction overlays, object validation states

• Versioning, undo history, and metadata tracking

Fast yet clinically precise, ideal for frontline workflows.

Review, Validation, and Quality Assurance

After annotation, data can be sent to review. Gesund.ai supports:

• Multi-phase validation

• Inter-rater agreement measurement

• Lineage tracking for annotations

• Model output comparison

These features go beyond visual QA; they help validate the annotation process itself, which is crucial for clinical-grade AI development.

How Gesund.ai Compares to other Medical Annotation Tools

Why Choose Gesund.ai?

Because building medical AI isn't just about drawing labels—it's about creating reliable systems that scale.

With Gesund.ai, you can:

• Upload once—no rework for modeling or review

• Annotate directly on model results

• Compare AI outputs with ground truth

• Track label history and object versions

• Move from annotation to validation to regulatory readiness—all in one place

Unlike fragmented pipelines, Gesund.ai ’s unified architecture ensures data integrity, reproducibility, and scale—from hospital research to clinical deployment.

Our “all-in-one” architecture ensures consistency across every stage. No disconnected systems. No duplicated effort. No surprises when it’s time to scale. This is how clinical AI becomes real—not in silos, but in symphony.

Final Thoughts

Reliable medical AI starts with tools designed for the job.

Gesund.ai unites annotation, prediction, validation, and compliance into a single, structured, and audit-ready ecosystem. Whether you're labeling organ boundaries or reviewing inter-observer agreement, we streamline the entire lifecycle of AI data—from pixel to publication.

Built for medical AI teams. Connected from day one. Ready for what’s next.

Want to see it in action? Book a demo and start annotating today.