The digital transformation of pathology has been long anticipated but slow to materialize due to technical, regulatory, and operational challenges. Now, advances in whole-slide imaging, artificial intelligence (AI), and automation are beginning to bridge the gap between potential and practice.
Here, Prasanth Perugupalli – Vice President and General Manager of Digital Pathology at Evident – discusses the field’s challenges and opportunities, offering a valuable perspective for pathology labs navigating this rapidly changing landscape.
Meet Prasanth Perugupalli
With a background in electrical engineering, I worked in the wireless semiconductor industry before venturing into imaging, microscopy, and software. In 2016, I co-founded and led a digital pathology company called Spectral Insights in Bangalore, India.
After a buy-out, I led the spin-out company, Pramana, as Chief Product Officer, and we successfully built it into a major supplier of digital pathology systems in the US. Evident acquired Pramana in September 2025, and I now have the privilege and opportunity to lead its digital pathology business.
We are bringing together Pramana’s cutting-edge autonomous whole-slide imaging technologies with Evident’s longstanding leadership in clinical and research microscopy and optical excellence, empowering pathology labs worldwide to embrace digital transformation and modernize workflows.
What's holding back digital pathology adoption, and how is the industry tackling it?
Technology adoption is rarely a straight line. When the value proposition isn’t immediately clear, competing perspectives emerge and alignment becomes elusive. Digital pathology has lived through this dynamic for nearly 15 years. Since glass slides remain the foundation of pathology workflows, early digital efforts largely centered on enabling pathologists to work remotely and sign out cases from afar.
The first generation of scanners prioritized image delivery speed – how quickly a digital slide could appear on a pathologist’s screen—even if that meant accepting compromises in accuracy and workflow efficiency. Laboratories absorbed the extra burden of manual slide handling and quality checks because these systems solved immediate problems like staff shortages and remote access.
But as digital pathology enters the mainstream of primary diagnostics, those early compromises are no longer sustainable. Added labor, software overheads, and workflow complexity are now triggering difficult conversations about scalability and return on investment.
The next wave – the second generation of digital pathology – is addressing these very issues. By embedding AI directly into imaging systems, these solutions automate quality control, optimize data capture, and enable smarter, more efficient whole-slide imaging. The shift marks a turning point: from merely digitizing slides to truly transforming pathology workflows through data-driven automation.
What do you see as the main drivers of innovation in digital pathology right now?
The strongest driver of innovation today is automation. Laboratories are increasingly recognizing untapped opportunities to streamline operations and eliminate inefficiencies. Modern automation technologies now enable seamless handling of diverse samples, minimizing variability well before slides are prepared for imaging. In-line, real-time analytics are taking this further – producing quality-assured images that are immediately ready for diagnostic and analytical use.
As these foundational challenges are addressed, innovation is shifting toward empowering the pathologist. The conversation is no longer just about digitization – it’s about democratizing pathology. Intelligent systems are allowing every pathologist to benefit from the collective expertise of specialists, driving greater consistency, accuracy, and accessibility across the field.
AI has become the defining catalyst of this movement. Over the past five years, AI has rapidly evolved from an experimental aid to a critical enabler of scalable, connected, and data-driven pathology.
How do you see AI reshaping digital pathology capabilities over the next five years?
Many pathologists express a strong desire for AI-assisted prescreening and triaging tools that can help them deliver faster, more accurate patient care. They want to minimize time spent on repetitive tasks and focus on cases that require deeper expertise and mindful presence. They also seek time for continued learning, research, and collaboration.
This is where AI will serve as a powerful enabler. Report generation – currently dependent on dictation and transcription – will soon be handled by intelligent AI agents. On the imaging front, we will see the tokenization of images, with AI systems classifying, enriching, and generating first-pass inferences. Early applications in tumor detection, glandular segmentation, and morphological assessment are already demonstrating what’s possible.
In the coming years, the focus will expand from answering single questions to performing multidimensional analyses at the case level. AI-driven whole-slide imaging systems will enable this seamlessly, supporting faster and more confident diagnoses.
Five years from now, imaging systems will look entirely different – robotic, adaptive, and intelligent. They will autonomously capture the most relevant images for a case, eliminating the need for technicians to adjust settings manually. Tissue imaging will effectively gain an “easy” button, freeing human expertise to focus on interpretation and insight.
This vision is closer than it seems. The core components already exist – the next big step is integration.
Which emerging technologies are you most excited about, and why?
Advances in robotics are particularly exciting – they’re inspiring a shift toward modular, compartmentalized system design that makes complex technologies more adaptable and manageable. The field is also borrowing principles from adjacent industries to build underlying architectures that better address the unique needs of digital pathology.
A pathology report typically draws from multiple data modalities – not just a single 2D image. Enabling this data to flow into a unified platform for triangulation and cross-verification is key to advancing diagnostic precision.
Meanwhile, AI-driven image enrichment and inference continue to accelerate, fueled by the semiconductor industry’s year-over-year computing gains. Combined with the growing accessibility of high-quality datasets – extending beyond elite research centers – this is unlocking innovation from a broader pool of developers and institutions than ever before.
Given that digital pathology uptake has been slower than anticipated in North America, what would make the most difference in expediting adoption?
Most pathology labs in the US are eager to digitize but remain cautious due to the challenges discussed earlier. Workforce shortages and economic pressures make efficiency critical, yet many current digital workflows add complexity instead of reducing it.
The key to accelerating adoption lies in transitioning from “best-effort imaging” to “quality-assured imaging.” When digital workflows integrate validated screening tools, they deliver tangible and measurable value to institutions. A comprehensive solution that combines a scanner, quality-control mechanisms, and accurate AI algorithms for identifying negative or positive cases benefits both high-volume institutions and smaller, resource-limited labs.
More importantly, value grows exponentially when digital pathology begins to mirror how pathologists and oncologists naturally approach a diagnosis – not one slide at a time, but across an entire case composed of multiple slides and contextual data. When digital systems integrate and present this information cohesively – linking related slides, clinical metadata, and relevant analytics – they transform the diagnostic experience from a digital copy of the microscope to a more powerful, unified, and insight-rich platform.
This convergence – delivering information in the way clinicians think, decide, and collaborate – marks a fundamental inflection point. As digital pathology aligns more closely with traditional diagnostic reasoning, adoption becomes self-reinforcing, because it’s not just about technology – it’s about enabling better medicine.
To what extent do you think DICOM compliance will solve interoperability issues in digital pathology?
The most critical need in digital pathology is standardized data delivery. In the analog world, data came as stacks of printed reports from various lab tests. In the digital world, it must arrive through a well-structured, interoperable software ecosystem.
DICOM – long established in radiology – is now emerging as the preferred standard in digital pathology. While some challenges remain in scaling DICOM to handle multi-magnification, multi-modal datasets and AI-derived annotations, its adoption has already reduced system integration timelines dramatically – from months to days.
This shift empowers labs to adopt digital workflows with greater confidence and flexibility. Moreover, DICOM is paving the way for seamless AI integration, enabling a hybrid future that balances cloud and edge computing – similar to the architectures now defining autonomous vehicle systems.
What is your view on the potential for digital pathology data partnerships between pharmaceutical companies and digitally enabled labs?
My view begins and ends with patient safety, ethical data use, and a commitment to progress. When guided by these principles, data sharing between digitally enabled labs and pharmaceutical companies can unlock tremendous value for research and clinical advancement.
Digital labs produce cleaner, better-structured datasets that can accelerate discovery and reduce sample waste. When data is used responsibly, these partnerships can drive breakthroughs in diagnostics, therapeutics, and precision medicine.
As an engineer at heart, I’m deeply optimistic about the future of fully digital labs – and I look forward to continuing to contribute to that vision.
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