If there was the only phrase that could describe the future of medicine, it would undoubtedly be “computer vision”. The application of computer vision in the medical field has proved highly successful over the last decades and it keeps enhancing the whole healthcare domain with every coming year. We are already familiar with the basic use cases of AI in engineering, the beauty industry, and business but what exactly AI does for the medical field, and is it really beneficial? Let’s look into that.

Medical Computer Vision Benefits Explained

It’s no surprise that all the medical advancements are almost utterly AI-induced. Among the most general advantages of incorporating computer vision into healthcare routine at all its levels are the following:


The ever-rising number of patients, especially during the pandemic, has revealed the issue of a lack of qualified medical staff who could make a quick and efficient diagnosis without having a patient wait for several days which could cost one’s life. For example, computer vision solutions recognition like the ones developed by InData Labs allow diagnosticians to help more people in the unit of time and avoid unnecessary time waste.

Improved Diagnosis Quality

The capabilities of a human eye, equipped with the top-notch knowledge base, education, and expertise are indisputable. However, nobody is ensured against the human factor that can result in misdiagnoses. We can’t say that machines never fail, yet the chance of a mistake is close to a 0.

Early Detection and Disease Forecasting

Another medical computer vision advantage lies in the existence of an enormous disease database containing not only data about separate single illness stages but also the progression and linear development of each particular case. In simpler terms, computer vision makes it possible to dip a disease in the bud without the need for guessing and saves a fortune on later costly treatments.

Application of Computer Vision in Medical Field

The usage of artificial intelligence in healthcare is usually divided into two large branches, properly medicine and forensics. The first domain encompasses complex texture analysis and computer vision medical diagnosis, the second one focuses on tasks related to data matching. Here are the most common medical computer vision use cases.

Computer Vision for Medical Imaging

A lot has been said about the benefits of modern machine vision consulting and medical image processing but let’s be more precise. Medical imaging comes in various forms ranging from ultrasound to computer tomography. If the work had to be still done manually, the process of digital image processing would take up to 8 steps to make an image readable for making a diagnosis. AI unloads diagnosticians from that burden and lets them spend more time with patients.

Real-Time Analysis of Medical Images 

Apart from the fast and accurate image processing, analysis, and detection of anomalies are the computer vision functions most modern doctors rely on. According to the research, any type of the above-mentioned medical images goes through the same process to detect an abnormality: the database contains sample images of deviated internal body structures, and the program compares two slides in search of their textural similarities. Then the results are shown to the diagnostician who makes the final decision on the basis of the digital information displayed before him.

Pose Estimation For Healthcare Apps

Lately, much attention has been paid to 3d computer vision for medical applications. With the pandemic outbreak, a lot of post-injury patients had to finish their recovery at home without professional supervision. To solve the case, computer vision has been used for creating apps based on human pose estimation. Such applications contain physical exercise plans for different types of post-trauma recovery and act as a professional supervisor signaling if there’s a mistake in exercise performance.

Smart Data Capture 

It has been estimated that the biggest amount of generated images are related to the medical field. Now imagine the number of papers that come through healthcare institutions. Digitalization and data capture made it possible to perform a safe transition from hours of manual work to minutes of typing and storing medical and personal patient data in the cloud.

The Bottom Line

Computer vision technology in the medical context is the main driving force of the field. It may seem that it has seamlessly incorporated into the domain, however, experts say that there’s still a number of obstacles that hinder the speed of innovation adoption. Among them is the lack of training image data, its low-resolution quality which results in inaccurate automated diagnosing. Obviously, there’s still a long way to go to prepare the ground for proper computer vision functioning but the result will be totally worth the struggle, whatever time it will take.