Artificial intelligence is becoming an increasingly important part of healthcare operations, helping organizations manage growing workloads, improve efficiency, and make better use of specialized resources. For large diagnostic laboratories, these opportunities extend beyond medical testing. AI can also support customer service, streamline administrative processes, and help employees focus on work that requires human expertise.
Quest Diagnostics offers an example of how AI and automation can be applied across different areas of a healthcare organization. In 2025, the company reported $11 billion in net revenues and processed approximately 244 million test requisitions. It serves roughly one in three U.S. adults each year and about half of U.S. physicians and hospitals. At this scale, even relatively small improvements in laboratory and customer service workflows can affect millions of interactions and a substantial volume of work.
The company’s broader Invigorate program achieved its annual target of 3% cost savings and productivity improvements in 2025. These results reflect the combined contributions of multiple initiatives, including AI and automation.
Quest has also made investments to expand its use of technology. In June 2024, it completed the acquisition of selected PathAI Diagnostics laboratory assets to advance AI and digital pathology. In March 2025, it announced a collaboration with Google Cloud focused on improving data analytics and exploring generative AI applications across research, customer experience, and operations. Several of those applications were still under exploration at the time of the announcement.
Two areas illustrate how Quest is applying AI to existing operations: digital cytology for Pap test slide review and AI-assisted customer service for calls and emails. Both demonstrate how automation can support high-volume workflows while keeping people responsible for important decisions.
Improving Pap Test Slide Review Through AI-Assisted Digital Cytology
Cervical cancer screening depends on the careful examination of Pap test slides by trained cytologists and pathologists. These specialists review cell samples to identify abnormalities that may indicate precancerous or cancerous conditions. Because the process requires professional expertise, laboratory staffing can influence how many slides can be reviewed and how quickly results are delivered.
Staffing remains an important challenge for laboratories. The American Society for Clinical Pathology’s 2024 vacancy survey found that the vacancy rate in U.S. cytology departments declined from 13.2% in 2022 to 11.6% in 2024. The 2022 survey also reported that filling cytology positions could take more than 12 months.
For laboratories managing large screening volumes, the challenge is finding ways to increase review capacity without compromising diagnostic quality.
Quest has been exploring how digital imaging and AI can support this work. During its April 2026 earnings call, CEO Jim Davis estimated that approximately 60% to 70% of the company’s AI and automation efforts were focused on laboratory operations. He explained that Quest looks for opportunities where work traditionally performed through a microscope can be captured digitally and analyzed with algorithms.
One example is the Hologic Genius Digital Diagnostics System, which Quest uses in cervical cancer screening. The system combines digital imaging with an AI algorithm designed to help identify potentially precancerous and cancerous cells.
The process begins when a digital imager scans a stained Pap test slide that would traditionally be examined under a microscope. A deep learning model then selects images of individual cells and cell clusters for a digital review gallery. It also provides a cell count that helps specialists assess whether the sample is adequate for evaluation.
Cytologists and pathologists review the selected images using high-resolution workstations. Although AI helps identify and organize the images for examination, qualified medical professionals remain responsible for reviewing the findings and making the diagnosis.
This distinction is important. The technology supports the review process rather than independently replacing the professional judgment required for diagnostic decisions.
The digital imaging infrastructure also supports secure remote sharing of cases. This capability can help laboratories distribute review work across different locations, although it is a feature of the digital imaging system rather than the AI algorithm itself.
What AI-Assisted Cytology Means for Diagnostic Performance
The potential benefits of digital cytology extend beyond making slide images available electronically. The technology may help specialists identify important abnormalities while improving the efficiency of laboratory workflows.
A four-site study involving 1,960 evaluable slides compared the diagnostic performance of the Genius system with manual review. The study reported sensitivity of 81.5% for high-grade lesions or worse with the Genius system, compared with 74.0% for manual review.
However, the system also showed a reduction in specificity. At the same threshold, specificity declined from 97.2% with manual review to 94.8% with the digital system. This difference was statistically significant.
These findings highlight an important tradeoff. The digital system detected more high-grade abnormalities, but its lower specificity could also mean that more slides without high-grade abnormalities are flagged for additional review. For healthcare organizations, evaluating AI performance requires looking at both its ability to identify concerning findings and its potential to generate additional work.
Quest has also reported improvements in other laboratory workflows involving AI and automation. In its 2024 annual report, the company described using AI to separate specimens with no evidence of microbial growth, allowing medical scientists to concentrate on samples more likely to indicate disease.
During its 2023 Investor Day, Quest reported that it had reduced its microbiology workflow from eight steps to two. The company also reported a 40% improvement in turnaround time for negative cultures and an approximately 40% reduction in the full-time staffing required for the workflow. These figures reflect the combined effects of automation and AI rather than the contribution of AI alone.
A separate retrospective study at ProPath, a Dallas-based pathology laboratory, examined Pap test workflows before and after implementation of the Genius Digital Diagnostics System. Average daily review volumes remained similar, but the number of cases reviewed per cytologist each day increased from 74.5 to 94.7. At the same time, the average number of cytologists working per day declined from 10.4 to 8.1. The time from specimen accession to final sign-out also decreased across diagnostic categories.
The results offer insight into how digital workflows may improve laboratory productivity. However, the study had limitations that should be considered. Hologic funded the research and employed one of its co-authors. The analysis covered a single laboratory and included approximately eight months of data following implementation. It also did not use biopsy data to confirm the increase in disease detection, and its before-and-after design could not isolate the specific contribution of AI to the workflow changes.
Quest has continued expanding the technology across its laboratories. In February, the company said it had implemented the Genius system at two laboratories and planned to introduce it at several additional locations that year. During its July earnings call, Davis reported that the system had recently been installed at two more locations, following implementations at approximately half a dozen sites over the previous year.
At its 2025 Investor Day, Quest projected that implementing Genius could improve resource productivity by more than 40%. However, its subsequent February and July updates described implementation progress rather than reporting a measured productivity result. The distinction between projected benefits and demonstrated operational outcomes remains important when evaluating the technology’s impact.
Using AI to Improve Customer Service Calls and Emails
Laboratory operations are only one part of Quest’s business. The company also manages a large customer service operation connecting physicians, hospitals, patients, laboratories, and its courier network.
Many of these interactions involve routine requests. Physician offices, for example, may call a central line to schedule specimen pickups. These repeatable requests create a significant volume of work for customer service teams, particularly at an organization serving thousands of healthcare providers.
Quest’s contact centers handled approximately 13.2 million interactions in 2024, compared with 12.2 million in 2023. Phone calls accounted for 71% of all interactions in 2024.
The company has also reported progress in reducing the number of calls required relative to testing volume. Its 2025 Investor Day presentation showed that calls per 100 test requisitions declined from 6.2 in 2022 to 5.5 in 2023 and remained at 5.5 in 2024. This represented an 11% reduction compared with the 2022 baseline.
Managing customer service costs is another consideration. During its third-quarter 2026 earnings call, CEO Jim Davis estimated that wage inflation would range from 3% to 4% in 2026. He said wage inflation creates at least $120 million in annual cost pressure for Quest, making productivity improvements an important part of its broader Invigorate program.
To address these challenges, Quest has described several AI applications supporting its customer service teams.
One application helps representatives retrieve answers more quickly. In its 2024 annual report, Quest said it was expanding the use of AI to help customer service employees access information and respond to inquiries more efficiently.
Another application involves a virtual AI agent that handles routine logistics calls. During its 2025 Investor Day, Quest also described voice recognition software that interacts with clients to schedule routine specimen pickups. According to the company, this technology reduces customer wait times and allows employees to focus on higher-priority requests. Quest has not confirmed whether the voice recognition software and the virtual agent mentioned in later earnings discussions are the same system, although both address routine logistics calls.
AI is also being used to help manage incoming customer service emails. Davis described how AI tools assist representatives by triaging and routing messages so that inquiries can reach the appropriate team more quickly.
Together, these applications support different parts of the customer service process. Routine spoken requests can be handled through a virtual agent, incoming emails can be categorized and routed with AI assistance, and representatives can use AI to find answers while continuing to handle live customer interactions.
Measuring the Impact of AI in Customer Service
Quest has shared encouraging figures regarding the performance of its customer service AI tools, although the company has not disclosed all the details needed to independently evaluate their overall impact.
Davis reported that the virtual AI agent has reduced routine logistics calls by up to 50%. The phrase “up to” is important because it describes a best-case result rather than an average reduction across all calls.
He also reported a 40% productivity gain during the first quarter of 2026 among customer service agents using AI to triage emails. However, Quest has not published the methodology or comparison group behind that figure.
The company has not identified the vendors or models powering these applications. It has also not disclosed the specific call types handled by the virtual agent, adoption rates, interaction volumes, implementation costs, or the precise contribution of customer service AI to the savings achieved through Invigorate.
AI has appeared consistently in Quest’s customer service reporting from 2023 through 2026, suggesting sustained attention to these applications. However, continued reporting does not by itself establish the full scale or financial impact of their deployment.
For organizations considering similar technology, Quest’s approach offers a useful way to think about how different customer interactions can be handled.
Routine, repeatable spoken requests are strong candidates for virtual agents because they often follow predictable processes. Written requests may benefit from AI-assisted classification and routing, while more complex resolutions can remain with human representatives. When customers need to speak directly with an employee, AI can provide support by helping the representative locate answers faster.
The measurement process is just as important as the technology itself. Tracking calls per 100 requisitions helps account for changes in testing volume. Measuring employee productivity against a defined baseline can show whether AI is improving performance. Where practical, comparing results with a similar group of employees who are not using the tools can provide stronger evidence of the technology’s contribution.
Key Considerations for Businesses Adopting AI
Quest Diagnostics’ experience highlights the importance of choosing workflows where digital information can support meaningful decisions. Before introducing an AI system, organizations need to understand whether the digital inputs preserve the details specialists require. In diagnostic laboratories, that means ensuring that digitized slides provide the information necessary for accurate professional review.
Businesses must also establish clear boundaries for automation. AI may be appropriate for selecting images, routing routine requests, or helping employees find information. Decisions that require specialized interpretation, clinical judgment, or complex customer resolution should remain with qualified professionals. Systems should also have clear escalation processes when information is incomplete or a case falls outside the expected workflow.
Finally, every AI application should be evaluated against its own operational baseline. Improvements in throughput, quality, turnaround time, and cost should be measured separately for the workflow affected by the technology. Program-wide savings are useful for understanding overall performance, but they do not establish how much value a particular AI tool contributes.
These considerations help organizations avoid confusing the introduction of AI with proof of its effectiveness.
Building More Efficient Healthcare Operations With AI
Quest Diagnostics demonstrates how AI and automation can support both specialized laboratory work and high-volume customer service operations. In digital cytology, AI helps organize and select images for professional review, while digital infrastructure makes it possible to distribute cases across locations. In customer service, virtual agents and AI-assisted tools help manage routine calls, route emails, and provide employees with faster access to information.
The broader lesson is that AI does not need to replace people to create operational value. In many cases, its greatest contribution comes from reducing repetitive work, improving access to information, and allowing specialists and employees to concentrate on tasks that require human expertise.
However, measuring success requires more than reporting productivity gains or projected savings. Organizations must examine diagnostic performance, customer experience, workflow efficiency, and the quality of the decisions supported by AI.
As healthcare organizations continue adopting intelligent technologies, the most effective strategies will combine automation with professional oversight and clear performance measurement. Quest’s experience illustrates how this balance can help large healthcare operations improve efficiency while keeping people responsible for the decisions that matter most.