Medical AI & Imaging Model Development

Medical imaging, real-time ultrasound and diagnostic AI, built with regulatory requirements in mind and tested to hospital reliability standards.

A medical model is not finished when it reaches a good validation score. It is finished when a clinician can rely on it on an ordinary Tuesday, on a scanner it was not trained on, with a patient whose anatomy is unusual. Our Healthcare AI practice is built around that standard: models that are developed with regulatory requirements in mind and tested to hospital reliability standards.

What we build

• Imaging models: super-resolution and denoising, including diffusion-based architectures for low-dose CT.
• Real-time medical video: detection and segmentation on ultrasound and endoscopic video, with temporal tracking for frame-to-frame consistency.
• Diagnostic support models that flag findings for review and report calibrated confidence.
• Registration pipelines that align images across modalities and time points.
• Validation tooling: evaluation harnesses, subgroup analysis and the documentation that a clinical or regulatory reviewer will ask for.

How we build clinical-grade models

The difference between a research model and a clinical one is mostly process.

Start from the clinical question

We work with clinicians to define what the model must get right and what it must never do. For lesion detection that usually means prioritizing sensitivity; for image reconstruction it means preserving diagnostic detail rather than producing images that only look clean.

Design for the data you will actually see

Medical data varies by site, device and protocol. We audit data provenance early, hold out data that reflects deployment conditions, and test robustness before optimizing for headline metrics.

Keep everything traceable

Datasets, preprocessing, model versions and evaluation results are versioned and reproducible. That traceability is what lets your regulatory and quality teams build their case on our work.

Leave the system with your team

We work with your clinicians and engineers throughout the project, then hand over the code, weights and validation harness so your team can run and retrain the system.

Where we have shipped it

• CT Scan Super-Resolution & Denoising: a diffusion-based architecture for simultaneous 4× super-resolution and denoising of low-dose CT scans, enabling diagnostic-quality images from 75% reduced radiation exposure, at a PSNR of 38.7 dB.
• Real-Time Endometriosis Detection: lesion detection and segmentation on endovaginal ultrasound video for Femnov, at near-live framerate, delivered as a containerized API and validated against clinical ground truth by expert gynecologists.

Both build on our broader Computer Vision practice, and on medical imaging research published with Siemens Healthineers and UCL.

Deployment considerations

• On-prem by default. Patient data stays inside the hospital network, and models are packaged to run on local GPUs.
• Latency as a clinical requirement. During a live exam, the inference loop has to keep pace with the video. We measure end-to-end latency, not just model runtime.
• Integration with clinical workflow. Outputs go where clinicians already look, and every prediction is logged for later review.
• Monitoring after go-live. We ship drift and performance monitoring so changes in protocol or equipment are caught early.

If you are a hospital, medtech company or research group looking for this kind of partner, see Healthcare & Life Sciences or contact us.