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.

The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

By : Sampada S M

Published on Mar 6, 2025

Manufacturing Predictive Analytics

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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Here’s How Computer Vision is Transforming Healthcare