Computer vision in the healthcare industry: use cases
Computer vision is gaining more and more momentum these days, in particular in the healthcare environment. Radiology, pathology, dermatology, ophthalmology, and other narrow-focused fields are shifting their methods towards innovation, often using those techniques falling under artificial intelligence.
Discussing statistics, the numbers speak volumes.
To draw a general market picture:
- The global computer vision in healthcare market size has reached $2.60 billion in 2024
- And might go to $53.01 billion by 2034, as by Precedence Research
The dynamics in the United States are also quite insightful:
- The US market size was valued at over $530 million in 2023
- Its worth is projected to surpass $14 billion by 2034 at a 35,43% CAGR
Computer vision in healthcare, an overview
It’s impossible to fathom all the technological advancements that have been introduced in the medical field. They did not only allow us to understand the anatomy of tissues, which structure the body, more precisely, but changed the approach to diagnosis and treatment.
This was largely achieved by using artificial intelligence, in particular computer vision along with deep learning. These provide the ability to acquire, process, analyze, and understand dynamic images and videos in real-time, thereby allowing early intervention.
Computer vision use cases in healthcare
Fracture and injury detection
CV solutions can process CT scans, X-rays, MRIs, and more to enable even subtle fracture and injury detection. By flagging early issues, these might be helpful during emergencies where every second matters.
If we’re talking about general scenarios, you can also explore these materials:
- How is artificial intelligence used in healthcare today?
- Computer vision image processing used in healthcare settings
Posture and movement analysis
CV solutions can assess human bodies in rest and motion to enable a precise posture and movement analysis. By spotting slight patterns – no matter whether walking, sitting, jumping, performing exercises, and more – these capture human motion without markers or sensors.
Learn more about how artificial intelligence drives camera-based pose detection for accurate MSK screenings:
- The benefits of implementing computer vision for guided MSK rehabilitation
- Pose detection: Computer vision for remote ATR rehabilitation
Tumor detection and classification
Another example – computer vision is great at spotting abnormal growths and figuring out what they mean. From formations in mammograms to tumors in magnetic resonance imaging, these algorithms can support faster diagnosis and treatment.
Surgical navigation and assistance
During procedures, computer vision can guide the surgeon by indicating a structure or suggesting other paths. Whether minimally invasive procedures or more complex operations, the algorithm can reshape used methods fostering better patient outcomes.
The benefits of integrating computer vision in healthcare
Computer vision medical imaging
Computer vision does not just capture provided images and videos – the technology can interpret and analyze. By identifying for the human eye invisible patterns and features, modern algorithms can highlight health issues healthcare professionals might not timely recognize.
It’s like a radiologist with another, hyper-vigilant set of eyes that never gets tired.
Disease detection before escalation
Computer vision is more than capturing written text and numbers – it helps to spot non-perceptible problems. By processing patient scans (X-rays, MRIs, CT scans, and others), some algorithms can identify alarming signs, including fractures and tumors, before they become obvious.
That means earlier diagnosis and treatment, and a better chance of beating dangerous conditions.
Increased safety
In the healthcare landscape, patient safety is everything, and with computer vision, you protect data better. The advantages are tangible – from monitoring patient progress to flagging incorrect deliveries of medication.
The technology is always on watch (in a good way) to ensure no mistake slips through the cracks during shifts.
Reduced workload across departments
Let’s face the truth – healthcare professionals are working at the maximum limit, no matter the department. And easing some repetitive, energy-consuming processes (for example, chart scanning) can change the game.
It’s not about replacing healthcare specialists – it’s about giving them enough space to focus on priorities.
The challenges of adopting computer vision for healthcare
Data privacy
Medical images are not just pictures – they’re deeply sensitive information, which requires robust protection. When adopting artificial intelligence, data privacy comes front and center.
So, for software development, make sure to partner with professionals with expertise to navigate this process.
Data needs
Smart solutions are similar to students at school – they’re only as successful at subjects as what they’re taught. When training advanced algorithms, we feed them thousands (even millions) of high-quality medical images.
Partner with trusted providers, or else you end up with a tool that’s just randomly guessing.
Limited generalization
Just because a model works great in one healthcare facility, doesn’t mean it’ll perform the same in another. Image quality, utilized equipment, patient demographics, different operations, and other influential factors make scaling a model quite hard.
Lacking standardization
Healthcare records are the wild west of formats, naming conventions, and other domain-specific structures. Without having established standards for how these records are organized, the integration becomes messy.
Computer vision in healthcare: market trends
Recently growing government initiatives are pushing healthcare leaders towards adopting computer vision. These involve prospective partnerships, policy support, as well as funding, all focused around prioritizing further research.
For instance, the famous National Institutes of Health (shortly NIH) has allocated substantial funds to studies. These focus on exploring machine learning, deep learning, and also computer vision for applications all across healthcare operations – diagnosis, treatment, and more.
Computer vision in medicine, real-world examples
Computer vision for accurate blood recognition and analysis
Abto Software’s R&D experts took upon a medical imagery solution to support breast cancer image processing. By employing computer vision, the solution can recognize pathological changes in microscopic blood images, thus enabling early detection.
The results:
- An increase in overall operational speed
- And decrease in both operational expenses and daily computational costs
CV-based self-diagnosis telemedicine application
– physical therapy and rehabilitation with camera-based pose detection
Our client was looking for partners who possess extensive expertise in implementing artificial intelligence – within former successful cooperation, we contributed by covering the integration of personal medical devices. This time, we entered the partnership to provide our expertise in camera-based pose detection and analysis.
The application is designed to support public health:
- The app first guides the patient step-by-step through a series of exercises and tracks every movement
- The app then sends the collected health metrics to the health expert to enable personalized strategies
AI-supported physical health assessment
– jump recognition and analysis using sensorless activity recognition
Our client has approached our team to upgrade an enterprise school platform by using artificial intelligence. The goal – to enable jump recognition and analysis to facilitate public health, in particular children’s health.
The concept:
- The student is watching a reference
- The student then performs the exercise as demonstrated
- Our system automatically compares and scores the execution
- And, as a result, the teacher can review the analysis for further health assessment and intervention
How we can help
Abto Software, two decades in the tech game, has gained extensive expertise in implementing computer vision. With dozens of smoothly delivered projects – including unique healthcare projects – our company can handle any challenge to unlock business growth.
If interested in applying computer vision to strengthen your current healthcare ecosystem, let’s cooperate!
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- Artificial intelligence, in particular advanced analytics
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