Here’s How Computer Vision is Transforming Healthcare

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.

Published on Jan 30, 2025

Updated on Feb 4, 2025

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success.

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

Here’s How Computer Vision is Transforming Healthcare