AI and Computer Vision for Healthcare and MedTech
Helping healthcare and medical technology companies improve patient experience, product usability, and clinical workflows with AI-powered vision systems.
Get in touchAI Solutions for Patient Care, Medical Devices, and Clinical Operations
We develop computer vision and machine learning systems that help MedTech companies guide patients, analyse visual data, and add intelligent features to healthcare products.
-
Face and Body Measurement
Estimate facial dimensions, body position, and other relevant measurements using standard mobile or device cameras, without requiring specialised scanning equipment.
-
Medical Device Fitting
Help patients select the correct device size and visually verify whether masks, wearables, orthopaedic products, and other medical devices are positioned correctly.
-
Surgical and Clinical Workflow Support
Apply computer vision to surgical systems and clinical environments to recognise instruments, analyse procedures, and support workflow documentation.
-
Remote Treatment Monitoring
Use camera-based analysis to monitor treatment activities, patient engagement, and potential signs of discomfort or difficulty during home-based care.
-
Medical Imaging and Visual Analysis
Develop AI systems for analysing clinical images, microscopy data, diagnostic recordings, and other visual information used in healthcare and life sciences.
-
AI-Enhanced MedTech Products
Add computer vision, machine learning, and intelligent automation to existing healthcare applications, connected devices, and digital care platforms.
Use Cases for MedTech
Examples of how Visage Technologies could help with machine learning, generative AI and autonomy in MedTech industry applications.
Incorrect device fit can affect comfort, usability, and treatment outcomes
Patients often need help selecting the correct size and positioning masks, wearables, orthopaedic products, and other medical devices. Manual fitting can require specialist support, delay delivery, and make home-based setup more difficult.
Camera-based fitting and positioning guidance
Computer vision and machine learning can use a standard mobile device camera to estimate relevant facial or body measurements, recommend an appropriate device size, and verify whether the device is positioned correctly.
Integrated into a healthcare application, the system can guide users through setup, provide immediate visual feedback, and flag potential fitting or positioning errors. This can improve patient experience, reduce unnecessary product exchanges, and support more scalable remote care.
- Computer Vision / Machine Learning
- Face and Body Analysis
- Measurement and Position Estimation
- Mobile AI
- Detection and Classification AI
- R&D
- Custom AI Development
Incorrect patient positioning can affect treatment setup and usability
Many home-based medical procedures require patients to position themselves, equipment, or treatment components correctly before starting. Without a clinician physically present, users may struggle to follow positioning instructions consistently or recognize when something is incorrectly aligned. This can increase the need for remote support, complicate onboarding, and reduce confidence in at-home treatment.
Camera-based positioning and setup verification
Computer vision can use a standard smartphone or tablet camera to analyse patient posture, body position, and the placement of relevant treatment equipment in real time. The system can compare the observed setup against predefined positioning requirements and provide immediate visual guidance when adjustments are needed. Integrated into a medical application, it can guide patients step by step through preparation while automatically identifying potential positioning errors. This enables more consistent home-based setup, reduces reliance on live clinician assistance, and supports scalable remote treatment workflows.
- Computer Vision / Machine Learning
- Body Analysis
- Pose and Position Estimation
- Mobile AI
- Detection and Classification AI
- R&D
- Custom AI Development
