AI now assembles much of that information. In some cases, a single image supplies enough information to populate multiple required fields at once. Work that previously required several steps of manual entry now happens earlier and more consistently. That shift changes how intake fits into the workflow. By the time a technologist steps in, the case already resembles a complete intake, allowing them more time to confirm accuracy and quality.

Teams at Mayo use AI to identify volume patterns and plan their schedules accordingly. AI pulls in information the lab already has, including procedures currently scheduled, how similar days have historically played out, and uses that to signal where demand is likely to spike. Instead of waiting for backup to appear, supervisors can redistribute work or adjust the order in which cases move through the lab. In some settings, the same approach extends to forecasting supplies, such as estimating how much blood will be needed for a given set of procedures.

AI helps organize the incoming data into a clearer view, pinpoints areas that may need attention, and establishes an initial frame for review. In practice, this means work involving repetitive fine-tuning now looks more like a quick confirmation step. The reviewer is still responsible for quality maintenance, but they are reviewing data against a prepared starting point rather than building one themselves.

AI provides the pathologist with a draft integrating all the relevant inputs. From there, the pathologist reviews, edits, and finalizes the interpretation. In digital pathology workflows, this can also include automatically tallying elements like positive and negative cells across large images and feeding those counts into the report. The result is less time spent constructing the write-up and more consistency in how results are presented.

Parting thoughts

Across Mayo Clinic Laboratories, staff are focused on using AI to strengthen the parts of the workflow that most directly support day-to-day operations, from front-end intake and workload balancing to reporting and interpretation. These enhancements don’t change the test itself, but they help staff streamline processes, improve consistency, and make it easier for teams to maintain performance as volume grows.

Read Mayo Clinic Laboratories’ full piece on how teams use AI across the workflow, from intake through interpretation.

Mayo Clinic Laboratories provides specialized laboratory testing for serious and complex conditions to advance patient care worldwide. For 55 years, Mayo Clinic Laboratories has helped physicians answer the toughest clinical questions with confidence and empowered hospitals to elevate care within their communities. Through an integrated diagnostics ecosystem powered by Mayo Clinic, the organization offers access to 4,300 tests and pathology services, delivering trusted, timely answers to clinicians and patients. Learn more here.

This article is sponsored by Mayo Clinic Laboratories, an Advisory Board member organization. Representatives of Mayo Clinic Laboratories helped select the topics and issues addressed. Advisory Board experts wrote the report, maintained final editorial approval, and conducted the underlying research independently and objectively. Advisory Board does not endorse any company, organization, product or brand mentioned herein.

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