Here’s How Computer Vision is Transforming Healthcare

Published on Mar 29, 2022

The scope of application of AI-driven technologies in Healthcare is increasing. It seems we are approaching a world where our connected devices tell us when we need to visit our doctor because they have detected symptoms that might be concerning. An explosion of data and computer vision technology has extended a helping hand to medical professionals in decision-making.

As per a report by Verified Market Research, computer vision in Healthcare Market was valued at USD 229.58 Million in 2018 and is projected to reach USD 5317.75 million by 2026, growing at a CAGR of 48.13% from 2019 to 2026.

Computer vision has been around for several decades, but it has recently become a hot topic in the healthcare industry. With the help of computer vision technology, medical practitioners are now able to deliver greater accuracy when it comes to diagnostic procedures, and they can even take care of patients remotely through Conversational AI bots and virtual assistants. This aids the healthcare workers and medical professionals to focus on important tasks that need human intervention as certain processes can be automated through these virtual assistants.

Applications Of Computer Vision in Medicine

Computer vision has drastically changed how doctors practice their art. From new technology that provides quicker diagnoses to wearables that continuously monitor vital signs and send out alerts if something is off—computer vision helps healthcare organizations provide better care delivery. Here is how computer vision can help augment healthcare services.

Cancer Detection

Early detection of cancer is significantly important for improving cure rates and survival rates. Traditional methods of diagnosing are largely inaccurate, however, there has been a recent upsurge in using computer vision to diagnose cancers such as skin, breast, ovarian, and prostate cancers. Computer Vision helps in carrying out in-depth analysis and early detection of grave diseases like cancer.

PathomIQ Inc. an AI-enabled computational analysis platform, wanted to enhance its Image processing techniques to allow earlier detection of abnormalities and treatment monitoring. Mantra Labs built and trained AI models on relevant medical data to find specific malignancy patterns that helped them in the detection of high-grade cancer cells.

Surgery

Today, surgeons can easily rely on medical imagery derived through cutting-edge technologies such as machine learning and computer vision for assistance during an operation. A simple task such as examining an x-ray of a broken bone when analyzed using computer vision can help improve surgical success rates by eliminating possible human errors. Further studies focus on applications of computer vision in monitoring chronic diseases, heart surgeries, and preventative care.

Dermatology

Computer vision is helping dermatologists in detecting skin cancers with high accuracy. AI algorithms can detect small abnormalities in images of skin lesions and determine which ones need biopsies. This helps avoid invasive procedures on healthy people and confirm diagnoses in those who need it.

According to a paper published in ScienceDirect by Umm AL-Qura University’s Department of Computer Science and Engineering, a method is offered for the dissection of skin illnesses utilizing color photographs without the requirement for medical intervention. The method had two steps, and the accuracy was remarkable at 95.99 percent for the first stage and 94.016 percent for the second stage when tested on six different forms of skin conditions.

What’s Next in Computer Vision?

There are a growing number of companies combining computer vision with AI technologies such as machine learning, natural language processing (NLP), and deep learning to develop innovative products that will transform medicine. For example, using self-driving vehicles for patient transportation. Combining computer vision with AI also means medical applications don’t need to be at medical facilities—they could be integrated into existing or future systems. Imagine simply plugging your smartphone into an algorithm designed to detect cardiovascular disease and having immediate results in real-time!

Though it comes with certain challenges such as lack of technical knowledge, hesitation to adopt AI-based technologies, the possibility of technical errors, dearth of skilled professionals, etc. However, with rapid digitization in the world, the application of these new-age technologies will grow exponentially.

Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform.

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization: Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

Here’s How Computer Vision is Transforming Healthcare