Computer vision in the healthcare industry: use cases

This post is a quick overview of an Abto Software blog article about computer vision in healthcare.

Computer vision is quickly becoming a game changer in healthcare. Fields like radiology, pathology, dermatology, and ophthalmology are increasingly turning to AI-powered tools to transform how diagnosis and treatment are approached.

The numbers show just how fast the technology is growing.

Here’s a quick snapshot:

In 2024, the global market for computer vision in healthcare hit $2.6 billion
By 2034, it’s expected to reach $53.01 billion, according to Precedence Research

The global computer vision in healthcare market size in 2023–2034 – by Abto Software

In the United States:

The market was valued at over $530 million in 2023
It’s projected to exceed $14 billion by 2034, growing at a 35.43% CAGR

Computer vision in healthcare market analysis, United States – by Abto Software

Computer vision in healthcare, an overview

The healthcare industry has seen remarkable technological progress. Thanks to innovations like computer vision and deep learning, we can now analyze human anatomy in greater detail and improve diagnostics and treatment strategies.

These tools allow healthcare providers to process images and video streams in real time. As a result, they can catch signs of disease earlier and intervene faster.

Computer vision use cases in healthcare

Fracture and injury detection

Computer vision systems can process X-rays, CT scans, and MRIs to detect fractures and injuries — even subtle ones. This capability is especially important in emergencies when every second matters.

Want a broader view? Check out:

Posture and movement analysis

By analyzing body movements and posture, computer vision helps detect physical issues without using sensors or markers. Whether someone is walking, exercising, or just sitting, these systems can identify even the slightest irregularities.

Tumor detection and classification

Computer vision can identify abnormal growths and help classify tumors by analyzing scans like MRIs and mammograms. This leads to faster and more accurate diagnoses, improving treatment outcomes.

Surgical navigation and assistance

During surgery, computer vision helps guide surgeons by identifying critical structures or suggesting alternative paths. Whether the procedure is minimally invasive or complex, this support can significantly improve results.

The benefits of integrating computer vision in healthcare

Enhanced medical imaging

Computer vision doesn’t just capture images; it understands them. Advanced algorithms spot patterns invisible to the human eye, helping doctors catch issues they might otherwise miss.

It’s like giving radiologists an always-alert, second pair of eyes.

Earlier disease detection

These systems can analyze scans and spot signs of illness — including fractures, tumors, and other abnormalities — well before they become serious. That means more time to plan and act.

Better patient safety

Computer vision contributes to safer care by tracking medication delivery, monitoring patient condition, and flagging potential issues during care. It reduces human error and enhances trust in clinical environments.

Lightened workload for medical staff

Healthcare professionals are stretched thin. Automating time-consuming tasks like chart analysis or data review helps reduce stress and lets doctors focus on what matters most: patients.

The challenges of adopting computer vision for healthcare

Protecting patient data

Medical images are sensitive and must be handled with strict data protection. When using AI, strong privacy practices and secure systems are essential.

Work with experienced development partners to ensure everything is secure and compliant.

High-quality training data

AI algorithms learn by example. For accurate results, they need access to thousands — or even millions — of well-labeled medical images.

Without the right data, your system might not perform as expected. Reliable data sources make all the difference.

Adapting models across environments

A model that works well in one clinic might fail in another. Different devices, image formats, patient populations, and processes can impact accuracy and reliability.

Standardization is key

Healthcare data comes in countless formats with inconsistent naming and storage methods. Without standard practices, integrating AI can become a tangled mess.

Computer vision in healthcare: market trends

Governments around the world are now actively encouraging AI use in medicine. Through policies, funding, and collaborations, they’re creating the perfect conditions for growth.

Take the U.S. National Institutes of Health (NIH), for example. It has been investing heavily in research that combines computer vision, deep learning, and AI for diagnosing and treating a wide range of conditions.

Computer vision in medicine, real-world examples

Recognizing blood abnormalities with computer vision

Abto Software’s R&D team developed a computer vision solution for breast cancer image analysis. It works by detecting pathological patterns in microscopic blood images — leading to quicker diagnoses and more efficient operations.

Results included:

Faster overall processing times
Lower operational and computing costs

Self-diagnosis in telemedicine using pose detection

We partnered with a client to build a telemedicine app for physical therapy and rehab. Drawing on our experience with AI and personal medical device integration, we implemented a camera-based pose detection system.

The app:

Guides patients through exercises
Collects movement data
Sends the data to doctors, who develop customized treatment plans

AI-enhanced physical health assessments for schools

Another client wanted to upgrade an educational platform with AI to support kids’ health. We built a system to recognize jumps and assess physical activity — all without using external sensors.

The system:

Shows a reference video
Records the student’s attempt
Compares and scores the performance automatically
Gives teachers detailed feedback for future health interventions

How we can help

At Abto Software, we’ve been working with AI and computer vision for over 18 years. Our team has built countless successful solutions, including many in the healthcare field.

We’re ready to help you apply computer vision to enhance your healthcare ecosystem.

Our services:

Our expertise:

Abto Software @abtosoftware