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Modern Medical Enterprises Absolutely Need Test Automation. Here’s Why.

3 minutes, 38 seconds read

The healthcare industry is getting a comprehensive digital facelift. Digital Health Systems (DHS) that use new digital technologies like artificial intelligence & robotics are delivering smarter healthcare services and better health outcomes to the masses. Health organizations are increasingly relying on them to improve care coordination, chronic disease management and the overall patient experience. These health systems are also alleviating repetitive administrative tasks from the roles of healthcare professionals, allowing them more time to practice actual healthcare.

The Modern Medical Enterprise draws on digital-enabled technologies such as telemedicine, AR/VR and remote-monitoring wearables to diagnose diseases and promote self-care. These applications rely on high-volume processing of patient data on a frequent basis.  Healthcare organizations also need to share/receive this information securely over a distributed network. However, sharing patient information remains a challenge, while the inability to access these records in a time-sensitive manner can affect the time-to-treatment for patients.

Deploying digital health systems that are both compliant to regulatory standards and functionally stable for a large number of concurrent users requires significant manned effort. Moreover, QA teams comprised of manual testers may end up working on repetitive manual test case scenarios that can lead to challenges in scaling or rolling out new features. 

How can the modern healthcare enterprise keep pace with issues posed by the safe deployment of their digital health systems? Automated Testing is a hallmark process of any digital transformation project. It gives enterprises the ability to shorten their release cycles and meet their business needs without affecting productivity or operations across the healthcare value chain. Test Automation also allows medical enterprises to run repeatable and extensible test cases against real-world scenarios.

Test Automation Use Case

The growth of DevOps and the rise of mobile-first applications are responsible for driving the growth of the test automation market globally. Today, enterprises are able to go faster-to-market owing to the technological advancements in quality assurance & testing.

For instance, in the case of a large US-based teleradiology firm that offers enterprise Imaging Solutions for improving patient care — a stable and reliable system mandated custom-built test automation frameworks. The medical technology company provides fast & secure access to diagnostic quality images using any web enabled device. To achieve this, they have built a cloud-based image sharing platform that allows digital image streaming, diagnostic & clinical viewing, and archiving for healthcare organizations.

Medical Image sharing among healthcare organizations is altogether brimming with security risks, and requires a complex network of systems to facilitate its smooth functioning. 

medical imaging system architecture
Medical Image Sharing Process among Healthcare Organizations

Also read – How are Medical Images shared among Healthcare Enterprises? 

In order to fulfil their business objectives, Mantra Labs identified key challenges for their testing requirements, namely —

1. Scalability
The platform must be able to support a high number of concurrent users.

2. Fail-over Control

The platform should behave functionally correct under very high loads with stable fail-over capability.


3. Efficiency & Reliability
The platform must scale rapidly when supporting a large user base & multiple formats with minimal page navigation response time.

Several testing components were deployed along with test automation techniques to address the full range of QA issues, including: functional testing, integration testing, GUI testing, and regression testing. 

Mantra Labs created a federated architecture to ensure near-perfect scaling, and true load & data isolation between different tenant organizations. The federated architecture consists of a number of deployments and a central set of components that stores global information like lists of organizations & users, and provides a centralized messaging service. 

test automation process flow diagram for modern medical enterprises
Mantra Labs Test Automation Process

Test Automation Improves Accuracy & Test Coverage

The entire cycle of bug detection in the UI, API and Server Loads involves several weeks of regression manual efforts. By automating tests, techniques like Stochastic Tests can be applied to detect bugs and reduce the overall cycle time.

Through Mantra Labs deep medical domain expertise, in-depth testing practices, intuitive suggestions for platform scaling and successful test automation efforts — significant business objectives were realised over the course for the client. Mantra was able to achieve over 60% reduction in cycle time, and about 65 per cent improvement in bug detection capability before the release cycle.

Nearly 35% of Executive Management objectives revolve around implementing quality checks early in the product life cycle, which can be achieved through test automation. For further queries and details about automated testing, please feel free to reach us at hello@mantralabsglobal.com

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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