AI in Clinical Labs: An Emerging Revolution or a Distant Promise?
This blog includes quotes from Guest Contributor – Bill Marquardt – Founder and CEO of MarquaTech, and CIO for Aqueous Diagnostic
AI for Laboratories: Future or Present
Artificial Intelligence (AI) is no longer a futuristic concept—it’s already reshaping industries by driving innovation, streamlining workflows, and changing how we interact with technology. In healthcare, its potential is especially striking. Yet, while AI’s promise for clinical laboratories is profound, its widespread implementation remains more aspirational than actual. For many labs, the integration of AI tools is still in its early stages, and meaningful adoption may take years—if not decades—due to practical, regulatory, and cultural barriers.
In the world of clinical diagnostics, AI offers an exciting frontier. With its potential to accelerate diagnostics, enhance accuracy, and improve efficiency, AI has sparked both enthusiasm and cautious optimism. But is AI truly revolutionizing laboratory medicine today, or are we still in the early stages of a longer journey?
The integration of AI into lab environments is steadily gaining traction. From intelligent imaging to predictive maintenance, AI is moving from concept to practical application. As Bill Marquardt, a laboratory IT expert and Founder and CEO of MarquaTech, and CIO for Aqueous Diagnostic,
notes:
“Laboratories are at the precipice of technological advancements not seen in over 20 years. This will require labs to re-think their workflows and use AI to maximize their operations beyond their traditional approaches to test and instrument management.”
This ongoing evolution prompts critical questions:
What real-world progress is being made?
What challenges remain before AI becomes mainstream in laboratories?
How do we balance the human touch with machine intelligence?
In this blog, we explore how AI is currently being applied in clinical labs, the roadblocks to widespread adoption, and what the future may hold. While the vision of fully AI-powered labs may still lie ahead, the steps taken today are shaping the path toward that future.
Current Applications of AI in Laboratories
1. Digital Pathology
AI is rapidly transforming Clinical Anatomical Pathology, particularly through digital imaging and data analysis. AI-powered digital microscopes now enable high-throughput scanning and pattern recognition that often surpasses the capabilities of the human eye. These systems can detect abnormalities in blood smears, pathology slides, and other diagnostic images with increased speed and precision.
A leading example is Mayo Clinic Laboratories, which has launched an ambitious project to digitize more than 20 million pathology slides, along with related data such as treatment notes, medications, and genomic profiles (Malloy, 2025). This massive dataset will serve as the foundation for AI-driven care models, offering powerful new tools for predicting outcomes and personalizing treatment strategies.
In clinical settings, AI can help identify complex diseases—such as cancer, infections, and genetic disorders—more rapidly, enabling earlier intervention. AI algorithms also support predictive analytics, flagging patients at risk for conditions like sepsis or chronic organ failure before symptoms even appear.
However, widespread adoption remains a challenge. The infrastructure and investment required to implement AI-driven digital pathology limit its accessibility, especially for smaller labs. For many, these technologies may remain out of reach for years to come.
2. Microbiology Diagnostics
The field of microbiology is rapidly evolving with the integration of artificial intelligence (AI) and digital imaging technologies. Among the most promising developments is the use of AI-driven software to manage complex datasets generated from isolated bacterial samples—especially when combined with mass spectrometry. As noted by Undru, Uday, Lakshimi et al., “AI-enhanced software using machine learning can be extended to traditional gram stains, ova and parasite analysis, and histopathology to move to identification and susceptibility testing faster.” This advancement signals a major shift in microbiological diagnostics, driving improvements in both efficiency and diagnostic accuracy.
The microbiology lab of the future is poised to streamline operations by automating repetitive tasks such as gram staining, agar plate inspection, and sub-culturing. By leveraging AI as a human extender, laboratories can mitigate staffing shortages while simultaneously building comprehensive databases to support faster and more accurate identification. These AI-enhanced systems not only accelerate the diagnostic process but also enhance antimicrobial stewardship by providing advanced insights to guide treatment decisions.
As Bill Marquardt notes, the future of microbiology diagnostics is rapidly evolving:
“I believe we’ll see a growing shift toward PCR-based nucleic acid amplification test (NAAT) testing and potentially next-generation sequencing (NGS), powered by AI models for pathogen detection, quantification, and antimicrobial resistance (AMR) analysis. These methods have already demonstrated significantly faster turnaround times—and therefore lower overall costs—compared to traditional culture and sensitivity testing. More importantly, they enable complex diagnostic capabilities to extend beyond specialized microbiology labs.”
As demand for faster and more precise results continues to grow, microbiology laboratories must integrate automation and AI expertise into their strategic planning. Embracing these technologies will be key to reshaping lab workflows, improving clinical outcomes, and meeting the evolving demands of modern healthcare.
3. AI-Powered Lab Automation
Over the last two decades, laboratory automation has evolved from simple robotic handlers to fully integrated automation lines. Now, AI is taking automation a step further—introducing intelligent routing and decision-making that enhances throughput and reduces errors.
AI-powered automation systems can prioritize urgent tests, adapt workflows in real time, and ensure better use of resources like reagents and quality control materials. These capabilities lead to faster test turnaround times, reduced waste, and improved staff productivity.
Still, the adoption curve is uneven. Larger labs with greater financial and IT resources are leading the charge, while smaller and mid-sized labs are more likely to upgrade incrementally—adding AI-powered instruments and modules over time as budgets and infrastructure allow.
4. Auto-Verification Rules Logic
For over two decades, clinical laboratories have relied on rules-based systems embedded in their Laboratory Information Systems (LIS) or middleware. These systems use lab-defined parameters—such as reference ranges and logical rules—to automatically release test results that fall within normal or pre-determined thresholds, including critical or panic values.
According to Bill Marquardt—also the author of CLSI AUTO15: Autoverification of Medical Laboratory Results for Specific Disciplines—the process of automated result verification, or autoverification, represents “the laboratory’s first real dip into AI.” While technically a form of deterministic AI, driven by predefined rules and algorithms, it greatly enhances workflow efficiency, reduces human error, and shortens turnaround times. Yet, as Marquardt points out, “many labs have yet to fully adopt or even embrace it.”
The limitations of auto-verification become more apparent in complex testing areas like molecular diagnostics and genomics, where traditional Boolean logic or simple if/then rules fall short. Here, machine learning (ML) using artificial intelligence offers a promising path forward. By enabling non-linear decision-making, ML can enhance the specificity of auto-verification, especially when combined with patient clinical data such as diagnoses and treatment history. This richer context could dramatically improve laboratory diagnosis, turnaround time, and patient outcomes. However, widespread implementation of such advanced systems remains on the horizon, as laboratories cautiously explore the next generation of auto-verification technologies.
5. Real-Time & Predictive Instrument Intelligence
Instrument manufacturers are embedding increasingly sophisticated software into their equipment, enabling real-time monitoring of operational metrics such as cycle counts, failure rates, and component usage. This shift allows for immediate detection of issues like calibration drift or component malfunctions—reducing errors and downtime.
AI is pushing this further through predictive maintenance. By analyzing patterns in usage and performance, AI can anticipate failures before they happen, helping labs avoid unplanned outages and reduce maintenance costs. This also extends the lifespan of equipment and optimizes staffing by minimizing emergency repairs.
Crucially, vendors are beginning to incorporate these features into smaller, more affordable instruments. As labs replace older equipment, many will gain access to these AI-enabled capabilities—ushering in broader adoption across facilities of varying sizes.
The question is no longer if AI will become standard in lab operations, but when and how. As technology matures, its role in assisting laboratory professionals will expand—taking over repetitive tasks and unlocking new opportunities for clinical insight.
Bill Marquardt puts it aptly:
“Laboratories have an opportunity to re-imagine their ecosystem and integrate AI into their infrastructure for long-term viability and durability as patient care pivots towards a more individualized approach.”
That said, the road ahead isn’t without obstacles. The pace of AI adoption will be shaped by cost, regulatory considerations, validation requirements, and the ability of lab teams to adapt. Implementing AI demands more than just technology—it requires a shift in mindset, investment in training, and careful change management.
While the fully AI-powered lab may still be a decade or more away, the journey has already begun. Labs that embrace AI in a thoughtful, scalable way will not only improve operational performance today but also lay the groundwork for a smarter, more responsive healthcare future.
Conclusion
So, is AI in clinical labs a revolution underway or a distant promise? The answer is—both. For some labs, particularly those with the resources to invest early, AI is beginning to provide tangible benefits. For others, the revolution will unfold more gradually, as costs decline, and technologies become more accessible.
What’s clear is that AI will play a pivotal role in shaping the future of laboratory medicine. By blending the power of machine intelligence with human expertise, clinical labs can achieve greater precision, speed, and scalability—ultimately improving patient care at every step
Reference
1. Malloy, T. (2025, January 13). Mayo Clinic launches Mayo Clinic Digital Pathology to modernize pathology, speed medical breakthroughs. Mayo Clinic News Network. https://newsnetwork.mayoclinic.org/discussion/mayo-clinic-launches-mayo-clinic-digital-pathology-to-modernize-pathology-speed-medical-breakthroughs/
2. Undru TR, Uday U, Lakshmi JT, Kaliappan A, Mallamgunta S, Nikhat SS, Sakthivadivel V, Gaur A. Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis - a Review. Maedica (Bucur). 2022 Jun;17(2):420-426. doi: 10.26574/maedica.2022.17.2.420. PMID: 36032592; PMCID: PMC9375890.
Contacts
You can contact our Guest Contributor- Bill Marquardt at:
760 High Road
Ashland, PA 17921
(570) 898 - 5440
info@marquatech.com
