Introducing AT Lab by Alive Technologies — Train Your Own Healthcare AI Model

Ivan Adji-Krstev
Apr 2
3 min read

You spent years mastering your specialty. You know what a good result looks like. You know the edge cases, the subtle signs, the things no textbook quite captures.

Now imagine an AI that learns directly from your expertise. That is what AT Lab does.

Why We Built AT Lab

Most AI tools in healthcare work the same way: a vendor trains a model on their dataset and ships the same product to every clinic. But no two clinics are the same. Different equipment, different patient populations, different grading standards. A model that works well in one lab often underperforms in another.

When that happens, clinicians lose trust. The AI gets ignored. Everyone goes back to doing things manually.

AT Lab takes a different approach.

How AT Lab Works

AT Lab is a platform that lets healthcare teams create, train, and manage their own AI models — using their own clinical data and their own expertise.

The workflow is straightforward:

Annotate — Your team labels clinical data through a simple interface. Grade an image, mark a structure, flag an anomaly. Every label becomes a building block for your model.
Train — When enough labels are collected, AT Lab trains a new model version automatically. No coding required. The platform handles everything behind the scenes.
Evaluate — Every new model is tested before it goes live. AT Lab shows clear performance metrics: is the new version better than the current one? Only models that pass evaluation get promoted.
Deploy — The best model is deployed and starts assisting your team immediately. The cycle continues — more labels, better models, better outcomes.

One Place for All Your Models

AT Lab gives your clinic a centralized hub where every model version is tracked, every training run is logged, and every dataset is versioned. You can see exactly how each model was trained, what data was used, and how it performed.

No scattered files. No versioning confusion. Full traceability — which matters when you work in a regulated environment.

Collaborate Across Clinics

AT Lab includes built-in collaboration features that let clinics share knowledge — not patient data.

Clinics can opt in to share de-identified annotations with the broader AT Lab community. An embryologist in one clinic benefits from labels created by a colleague in another country. A rare case seen once at one center becomes a training signal for everyone.

All sharing is consent-based, de-identified, and privacy-compliant. The result is a pooled dataset that no single clinic could build alone — feeding models that are stronger and more robust than anything one team could create on its own.

Specialists can also evaluate each other's models. A model trained at one clinic can be tested against another clinic's data. Peer review, but for AI.

Quality Controls Built In

Clinical AI is only as good as the data behind it. AT Lab includes quality control at every step:

Review workflows — Annotations are reviewed and approved by senior staff before they enter a training dataset.
Agreement tracking — When multiple experts label the same case, AT Lab measures how much they agree. High agreement means reliable labels. Disagreement flags cases for discussion.
Immutable datasets — Every dataset used for training is locked and versioned. You can always trace back exactly what data produced which model.

Works Across Healthcare — Not Just IVF

We built AT Lab in the IVF space, where embryologists use it to grade embryos, detect developmental stages, and track morphokinetic events. But the platform is designed to work across any healthcare specialty where experts assess images or video.

AT Lab is ready for:

Pathology — grading tissue samples
Radiology — detecting findings in medical imaging
Dermatology — classifying skin conditions
Ophthalmology — analyzing retinal scans
Any clinical field where visual assessment drives decisions

If your team can label it, AT Lab can train on it.

Why AT Lab Is Different

The bottleneck in healthcare AI has never been the algorithms. It is the data, the labels, and the trust.

AT Lab solves all three:

Your data — each clinic trains on its own cases, so the model fits your reality.
Your labels — created by your domain experts, not outsourced annotators who have never worked in your field.
Your trust — when you train the model yourself and watch it improve with every correction, you trust the output.

AT Lab is not here to replace clinicians. It is a tool that learns the way you do — from experience, from correction, from collaboration.

Get Started

AT Lab is built by Alive Technologies (AT). Whether you run an IVF clinic, a pathology lab, or any healthcare practice that relies on visual assessment — we would love to show you what your team can build.