AI for Hospitals, Medtech & Life Sciences

Medical imaging, real-time ultrasound AI and diagnostic systems built to hospital reliability standards. We have built real-time endometriosis detection on ultrasound video, CT super-resolution and registration pipelines with Siemens Healthineers and academic partners.

Resolution recovery

4×

Radiation dose reduction

75%

Ultrasound video inference

Near-live

Healthcare buyers have seen many AI pilots that never reach a patient. The reasons are rarely about model accuracy alone. Data varies by site and scanner, clinical workflows leave little room for a new screen, hospital IT will not move patient data off-site, and the evidence needed for regulatory and clinical sign-off is often missing. We build for hospitals, medtech companies and life-science research groups with those realities in view.

The problems we see in healthcare and life sciences

• Image quality against patient safety. Better images often mean more radiation or longer scans. Imaging teams want diagnostic quality with less of both.
• Decision support during the exam. Ultrasound and endoscopy happen in real time. Any assistance has to keep pace with the video and fit into the view the clinician already uses.
• Data that does not generalize. Models trained at one site degrade at another because of differences in devices, protocols and patient populations.
• Research that stays in the lab. Academic and R&D groups produce promising methods that never become validated, deployable tools.

What we build for hospitals and medtech

• Imaging reconstruction: super-resolution and denoising for modalities such as CT, recovering detail from lower-dose or lower-quality acquisitions.
• Real-time medical video: detection and segmentation on ultrasound and endoscopic video streams, with temporal tracking so results stay stable from frame to frame.
• Diagnostic support that flags findings for clinician review.
• Registration pipelines that align images across modalities and time points.

How we build these to clinical standards is covered in Healthcare AI, and the underlying vision work in Computer Vision.

Constraints we design for

Clinical reliability

We evaluate on the metrics clinicians use, test on held-out data that reflects deployment conditions, and document known limitations. Datasets, models and results are versioned so your quality and regulatory teams can build on them.

The hospital perimeter

Models are packaged to run on-prem inside the hospital network, so patient data stays where your governance already applies.

Latency in the loop

For real-time video, we measure from frame capture to display, not just model runtime, and design the model and serving path around that budget.

Relevant work

• CT Scan Super-Resolution & Denoising: a diffusion-based architecture delivering 4× resolution recovery and denoising of low-dose CT, enabling diagnostic-quality images from 75% reduced radiation exposure.
• Real-Time Endometriosis Detection: lesion detection and segmentation on endovaginal ultrasound video for Femnov, running at near-live framerate and validated in a POC reviewed by expert gynecologists.

Our team has published peer-reviewed medical imaging research with Siemens Healthineers and UCL, and we co-author with Harvard and UC Berkeley.

How to start

Most engagements begin with a focused clinical question and an existing dataset. We spend the first phase with your clinicians and engineers defining what the model must get right, auditing the data and agreeing on validation criteria. Then we build, validate on your data and deploy inside your environment, leaving your team with the code, weights and validation harness. Contact us to discuss a clinical or product use case.

Selected partners: Siemens Healthineers · UCL · Harvard