Henry Schein One rolls out AI X-ray checks across 10,000 clinics

Henry Schein One has launched Image Verify, an AI system for checking X-ray image quality, built on Amazon SageMaker AI. The system analyzes images in real time, at the moment of capture.

How Image Verify works

Image Verify is an AI model that automatically assesses the quality of dental X-ray images. Instead of leaving the check to a person, the system returns an instant verdict: the image is usable, or it needs to be retaken. This matters because a poor image leads to an inaccurate diagnosis and retakes, which delay treatment and increase the patient's radiation exposure.

The system runs on Amazon SageMaker AI, the AWS cloud platform for ML models, which lets it scale without infrastructure bottlenecks.

Who is Henry Schein One

Henry Schein is a global leader in supplying equipment and software to dental and medical clinics (the company is part of the Fortune 500). Henry Schein One is its cloud platform for managing a dental practice, used by tens of thousands of clinics worldwide. The platform unifies work with patients, X-rays, records and billing.

Scale of the rollout

Image Verify's progress is striking:

• 10,000+ active locations within a few months of launch
• 11+ million X-ray images already processed
• 1.5 million images per week (current processing rate)
• Plans to expand to 40,000 locations across four regions

This pace of adoption shows the product was ready and clinics wanted it.

Why X-ray quality checks are critical

In dentistry, X-ray images are a key diagnostic tool. But a poorly centered image, too little or too much exposure, or patient movement all degrade the diagnostic value. When an image is bad, the clinic faces a dilemma: retake it (extra radiation for the patient and extra chair time) or work with incomplete information and risk an error.

The AI system catches problems instantly, while the patient is still in the room, so the image can be retaken on the spot.

What this means

Image Verify is a classic example of production ML in healthcare: not a hypothetical task, but a system built into a real workflow. Henry Schein One has shown that cloud ML on Amazon SageMaker scales to tens of thousands of points of use without rebuilding the infrastructure. Going from concept to 10,000 clinics in months is a pace that once seemed unattainable in medtech.

It is also a signal to other medical software vendors: to compete in the cloud, you need ready-made ML models that can be integrated in days.