Artificial intelligence (AI) is reshaping laboratory medicine, not as a distant promise, but as a practical tool improving diagnostics, workflows, and quality. Through collaborative development, rigorous validation, and a commitment to ethical innovation, Mayo Clinic Laboratories is implementing AI-driven solutions that enhance precision, reduce turnaround times, and empower laboratorians to focus on what matters most: patient care.
In the evolving landscape of healthcare, laboratory medicine is undergoing a quiet revolution. Often operating behind the scenes, the clinical laboratory is the foundation of diagnosis, monitoring, and treatment decisions. At Mayo Clinic Laboratories, artificial intelligence (AI) is now playing a central role in transforming how labs operate, not just by automating tasks, but by enhancing precision, improving quality, and expanding access to expert-level diagnostics.
This transformation is not driven by hype, but by necessity. Rising test volumes, workforce shortages, and increasingly complex data have created an urgent need for smarter, more scalable solutions.
“Innovation means finding creative ways to tackle problems both new and old,” says William Morice II, M.D., Ph.D., president and CEO of Mayo Clinic Laboratories. “In laboratory medicine, that means using tools to get the right information to the right person while doing it in a way that enhances accuracy and efficiency.”
AI in the lab: From theory to practice
One of the most immediate impacts of AI in lab medicine is in workflow transformation. Tasks that once required hours of manual review, such as flow cytometry analysis, can now be completed in minutes with greater consistency and fewer errors.
“Algorithms now organize and evaluate data in ways that used to take hours,” says Dr. Morice. “That means patients get answers faster, and providers get more reliable information to guide care.”
These improvements aren’t just about speed. They’re about quality and consistency, two pillars of laboratory excellence. AI tools are helping standardize processes that were previously subjective, such as visual inspections for hemolysis or lipemia, and flagging pre-analytic defects earlier in the workflow.
“The earlier you catch a defect, the less impact it has on turnaround time,” says Christopher Garcia, M.D., chief digital information officer for Mayo Clinic Laboratories. “It’s better for the patient, better for the lab, and better for the care team.”
Dr. Garcia emphasizes that these tools are not standalone solutions; they’re part of a broader effort to modernize laboratory operations. “We’re seeing tools that take the mundane off our plates,” he says. “That means laboratorians can spend more time on interpretation, consultation, and quality assurance, the things that truly require human expertise.”
Precision through integration
Modern laboratory medicine is increasingly data-rich, with results spanning molecular diagnostics, histology, microbiology, and more. AI helps integrate these diverse data streams to support precision diagnostics, tailoring insights to individual patients rather than population averages.
Dr. Garcia describes this shift as moving from reactive to predictive, siloed to integrated, and generalized to personalized. “AI allows us to combine lab data with imaging, genomics, and clinical context to generate insights that are truly patient-specific,” he explains.
This is especially powerful in areas like cancer diagnostics, where predictive models can help determine how a patient might respond to a specific treatment, not based on broad trends, but on their unique biological profile.
“Precision in diagnostics usually leads toward personalized medicine,” Dr. Garcia says. “AI enables us to crunch through massive datasets and tie them back to the patient context in ways that were previously impossible.”
Empowering the lab workforce
AI is also helping address one of the most pressing challenges in lab medicine: workforce shortages. By automating routine tasks, such as document review, case triage, and data summarization, AI frees up staff to focus on higher-value work.
This is especially important in high-volume labs, where efficiency and accuracy must go hand in hand. AI doesn’t replace the laboratorian; it augments their capabilities, allowing them to do more with less.
“We’re seeing tools that automate routine data movement and decision-making,” Dr. Garcia notes. “That means staff can focus on higher-value tasks, the kind that require human judgment, empathy, and expertise.”
Dr. Garcia also points to the potential of agentic AI, autonomous systems that collaborate like human teams, to further scale expertise across the lab.
“Our technologies generate more information than any individual can comprehend,” says Dr. Morice. “Agentic AI will help us unlock new possibilities and improve care at scale.”
Building trust through collaboration
At Mayo Clinic Laboratories, AI development is rooted in cross-disciplinary teamwork. Lab professionals, data scientists, and clinicians work together to ensure that tools are designed with real-world workflows in mind.
“AI should be a colleague, not a replacement,” Dr. Garcia emphasizes. “The most successful solutions are those developed with the end user in mind, tools that support decision-making without disrupting care.”
This collaborative model ensures that AI tools are not only technically sound but also clinically meaningful. It also helps build trust among staff, a critical factor in successful adoption.
Dr. Garcia recalls a recent project where oncology clinicians, pathologists, and data scientists collaborated to develop AI-generated summaries of complex pathology reports. “The goal was to reduce cognitive load and improve communication,” he says. “It’s still in development, but it’s a great example of how shared goals and clear communication can drive meaningful innovation.”
Making the Promise of AI a Reality in Laboratory Medicine and Pathology
This webinar explores how artificial intelligence (AI) and digital pathology are transforming clinical laboratories by enhancing diagnostic speed, accuracy, and precision. While these technologies offer powerful tools for early disease detection and personalized medicine, their adoption faces challenges such as regulatory hurdles, data management, and workflow integration. Learn how collaboration and strategic implementation can help labs overcome these barriers and unlock the full potential of AI-driven diagnostics.
Ethics and the Mayo Model
As AI becomes more embedded in healthcare, questions of ethics, transparency, and equity are coming to the forefront. At Mayo, these aren’t afterthoughts; they’re foundational.
“If we stay true to our values of respect, integrity, compassion, stewardship, it doesn’t matter what tool we’re using,” says Dr. Morice. “It will be within the moral and ethical guardrails of patient care.”
Dr. Garcia adds that skepticism is healthy and necessary. “You should never blindly trust a solution,” he says. “We’re building forums for questions, encouraging curiosity, and working through concerns together.”
What’s next for laboratory medicine
Looking ahead, both Dr. Garcia and Dr. Morice see AI becoming an integral part of laboratory medicine, not just in diagnostics, but in operations, quality control, and decision support.
Dr. Garcia envisions a future where AI is seamlessly embedded in lab workflows, helping labs anticipate needs, optimize staffing, and improve turnaround times. “In five years, I hope AI is seen not as a novelty, but as a necessity,” he says. “A critical part of how we deliver the best care possible.”
Dr. Morice agrees, noting that the lab is uniquely positioned to benefit from AI. “Labs touch everything from cradle to grave,” he says. “The more we innovate, the more we can drive meaningful change in healthcare.”
Explore how Mayo Clinic Laboratories is harnessing AI and big data to transform diagnostics in this insightful conversation with Dr. Garcia on the Becker’s Healthcare Podcast.
