Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(43)

Solar Industry(8)

User Experience(66)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(7)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(143)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(19)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

Is AI Ready to Replace Your Doctor?

By :

Have you ever wondered what if doctors could harness the power of many experts, all at once? Imagine every heartbeat, every lab result, and every medication being processed in seconds—faster than any human could ever dream of. No, this isn’t science fiction; it’s the new reality of Artificial Intelligence (AI) and Large Language Models (LLMs) in healthcare. The rise of AI in medicine and medical artificial intelligence is transforming the landscape of patient care and research.

Think of AI as the invisible co-pilot in a doctor’s journey—an entity that never sleeps, forgets nothing, and spots patterns that would take years for a human mind to recognize. It’s like giving healthcare professionals superpowers, enabling them to stay ahead of the curve in ways we never thought possible. But the real magic? Smart alert mechanisms jump into action when things are about to go wrong, providing warnings that save lives and make sure the right decisions happen in real-time. This is where AI for medical diagnosis truly shines, enhancing the capabilities of healthcare professionals.

AI and LLMs are changing the way healthcare works—and we’re at the forefront. Here’s how.

AI Pathology: Microscope with Superpowers

What if your microscope could not only analyze slides but also interpret them? That’s exactly what we did for Pathomiq. Our AI-powered pathology tool doesn’t just scan whole slides—it identifies disease progression and predicts patient responses with unmatched precision. By integrating LLMs, we created a system that not only analyzes images but also generates comprehensive, easy-to-understand diagnostic reports.

For Pathomiq, we trained AI models to detect malignancy patterns with 99% accuracy, and the LLMs translated the results into meaningful insights for doctors, which benefitted them with Faster diagnostics, better accuracy, and simpler communication between specialists.

Medical Image Analysis: X-Rays, But Make It Smart

X-rays, MRIs, and other medical imaging can be a treasure trove of data, but they often need an intelligent eye to make sense of it all. Abbvie came to us with this challenge. Our AI models analyze medical images to pinpoint abnormalities, demonstrating the power of AI medical diagnosis.

AI takes care of the image recognition, while LLMs convert findings into plain language summaries. For Abbvie, this resulted in faster image processing and more accurate interpretations. Clearer insights, faster decisions, and a smart system that even non-experts can understand.

AI Health Advisors

Imagine a health advisor that predicts your next treatment before you even need it. Our AI health advisor uses predictive analytics to identify patients likely to undergo surgery, showcasing how AI forecasts patient outcomes. This is similar to the Nura AI health screening concept, where early predictions combined with actionable, easy-to-read insights mean better health outcomes and proactive care.

Intelligent Document Parsing

Medical documents are notorious for their jargon-heavy content. But what if AI and LLMs could automatically extract the relevant information? That’s exactly what we did with our intelligent document parsing tool. Whether research papers or patient reports, our system extracts key data and presents it in a clear, concise format.

AI handles document parsing for faster decision-making. As there wouldn’t be any more sifting through endless documents—It streamlines the process and saves time.

Drug Discovery: Abbvie’s Fast-Track to Innovation

When Abbvie sought to enhance its drug discovery process, we stepped in with an AI-powered platform that redefines speed and accuracy. We developed a research tool that lists genes with their weighted interconnectivity from research papers, providing a visualization framework to display genes and proteins along with their interconnections. Our AI tools handle complex text parsing across various document formats and perform frequency determination and spectral clustering to identify gene pairs, their locations, and contextual details.

Our AI extracts and visualizes gene data, parses text, and determines the frequency and clustering of gene interactions. This approach accelerates drug discovery, cuts costs, and offers a clearer path from genetic research to real-world drug development.

Clinical Trials: Pathomiq’s AI-Powered Cancer Detection

Clinical trials are all about accuracy and speed, especially in cancer detection. For Pathomiq, we built AI models that analyze digital slides to identify early-stage malignancies. Our AI stepped in to explain the findings and suggest the next steps, streamlining the process for researchers and doctors.

AI detects cancer patterns in digital pathology slides and provides context-rich explanations that make trial results easier to understand. Early cancer detection paired with simplified trial documentation means faster, more accurate results.

Conclusion: AI & LLM—The Future of Healthcare, Today

At Mantra Labs, we’re not just integrating AI and LLMs into healthcare; we’re pioneering a revolution. It is said that AI has the potential to reduce diagnostic errors by up to 30% and streamline drug discovery processes by cutting research times in half. It has revolutionized healthcare by delivering faster diagnostics, improving the accuracy of medical imaging, and optimizing processes like pathology and clinical trials. Yet, even with these advancements, the human touch remains essential. Healthcare professionals bring the empathy, intuition, and ethical judgment that AI, for all its precision, cannot replace. While AI enhances decision-making and efficiency, it’s the collaboration between human insight and machine intelligence that ensures the best outcomes. The future of healthcare is not just about smarter technology, but about how human expertise and AI together can provide faster, more precise, and compassionate care.

Further Reading:

Doctor Who? AI takes center stage in American Healthcare

Cancel

Knowledge thats worth delivered in your inbox

Why Netflix Broke Itself: Was It Success Rewritten Through Platform Engineering?

By :

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.

Source: getport.io

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot