Is Mixed Reality on the Horizon for Healthcare?

Published on Apr 1, 2022

Mixed Reality (MR) also known as “hybrid reality” and “extended reality,” has the potential to change just about every industry, healthcare being no exception. A combination of Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI); MR is emerging as a tech to create experiences that blend the real-life environment with digital elements.

It is lauded as being revolutionary because of its ability to provide a more personalized and immersive experience and recent advancements are paving way for previously unimagined possibilities in medicine, not only by lowering training and operational costs but also by improving surgical safety and precision.

According to a report by Research and Markets, the mixed reality market was valued at USD 376.1 million in 2020 and is expected to reach USD 3,915.6 million by 2026 with an expected CAGR of 41.8% over the forecast period 2021 to 2026.

With the rapid adoption of Mixed Reality in the coming years, the technology could find a variety of uses in the healthcare sector, including reducing the use of cadavers in medical student training, patient engagement therapy for post-traumatic stress disorder (PTSD), and pre-operative visualisation of brain tumours by reviewing scans in-person using AR.

How ‘Mixed Reality’ is reshaping complex health procedures?

Mixed Reality offers infinite possibilities in medical diagnosis, training, surgeries, medical treatments, and rehabilitation, making it extremely detailed and accurate.

Instant diagnoses

MR headsets can record patient history discussed verbally by medical professionals which can be accessed by anyone including the nursing staff. Furthermore, these headsets can even analyze data and provide reports to doctors in real-time, eliminating the need to manually go through physical reports, making diagnosis faster and more accurate.

Medical training

Mixed Reality in recent years has seen more popularity in academics where it acts as an aid for teachers to teach various subjects and techniques. Students too can hone their skills before performing surgeries on patients. Doctors can also use MR to rehearse complicated surgeries, saving valuable time during their procedure while increasing their success rate.

Enhanced surgery

MR develops personalized 3D models for each patient and visualizes the interior anatomy in a completely immersive environment, thereby helping in pre-operative simulations. The MR wearable devices in combination with new emerging imaging technologies can aid greatly in complex surgical procedures such as reconstructive surgeries where holographic overlays helped surgeons to better view the bones and identify the course of blood vessels.

Recent applications of Mixed Reality in healthcare

Renowned medical universities are researching and using mixed reality in different areas of medicine, and the results appear to be promising in cardiology, training, autism, surgery, and more.

Cardiology:

Apollo Hospitals, one of the largest hospital chains in India, launch a mixed reality programme- Apollo ProHealthDeepX that uses machine learning, digital signal processing, and mixed reality to provide a visual insight into the internals of the heart using 3D images and assess a patient’s risk factors for heart disease all using the MR headsets.

Medicine Training:

NUS Medicine (Singapore) created Project Polaris which aims to integrate MR into their learning experience and create a realistic clinical scenario and give students a visual presentation of actual clinical procedural skills like inserting a cannula, as well as inserting catheters in male and female urinary tracts with the help of 3D holograms projections.

Autism Treatment:

The autism glass project of the medical school of Stanford University uses Google Glass to assist autistic children in interpreting their emotions and automating facial expression recognition using AI. They also intend to improve its accuracy and allow users to interact with it without the use of glasses in the future.

Phantom Limb Pain Treatment:

Aalborg University in Denmark conducted a study to examine if virtual reality (VR) can help reduce the pain of phantom limbs by tricking the amputee’s brain into believing it still controls the missing limb. When a patient moves his arm, the virtual arm moves in lockstep with them, allowing the patient to control the amputated limb with his brain.

Why the hesitation to implement MR?

Mixed Reality can be used in a variety of situations in healthcare, from home care to acute care units. While MR technology is expected to save costs and increase patient outcomes and satisfaction, healthcare professionals are encountering several challenges as they prepare to implement it.

The lack of adequate skill among medical practitioners, high investment costs, technical glitches, establishing interoperability with existing systems, defining reimbursement schemes, creating a secure environment, and the fear of data loss are all likely to stifle market growth for the time being during the assessment period.

The Road Ahead

Despite these challenges, over the projected period, improvements in regulatory policies are expected to ease the adoption of this technology. Factors such as rapid advancements in sensor technology, increased user acceptance, growing applications of MR in medical treatment, and increased workload of healthcare workers are driving the adoption of mixed reality in the global healthcare market. The benefits of MR systems, such as better operational efficiency, improved service quality, and reduced human effort, are also expected to boost mixed reality’s rise in the healthcare sector.
Statista report estimated that in 2025, the global mixed reality market will increase to about 3.7 billion U.S. dollars and the healthcare sector will hold the majority. It won’t be a surprise to see hospitals and clinics doubling the use of Virtual Reality (VR), Augmented Reality (AR), or Mixed Reality (MR) technologies in their clinical activities. Soon, we can expect to see MR technology being used in every other doctor’s clinic.

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?

Is Mixed Reality on the Horizon for Healthcare?