Artificial intelligence is becoming a larger part of healthcare, and the laboratory is no exception.
From analyzing complex data to supporting diagnostic processes and improving workflows, AI has the potential to change how laboratories operate. But adopting new technology is only one piece of the equation. Healthcare leaders also need to consider what AI means for quality, oversight, operations, and the laboratory workforce.
As AI becomes more integrated into laboratory medicine, successful implementation will require more than choosing the right technology. It will require strong processes and skilled laboratory professionals who understand how to use, evaluate, and oversee it.
A recent policy report from the Association for Diagnostics & Laboratory Medicine (ADLM) highlights several important considerations surrounding AI in laboratory medicine, including data quality, validation, oversight, and workforce development.
For healthcare and laboratory leaders, these areas provide a useful framework for preparing for what comes next.
1. Reliable AI Starts With Reliable Laboratory Data
AI systems depend heavily on data. In laboratory medicine, the quality and consistency of that data are especially important because laboratory information can directly influence clinical decisions.
One challenge is that laboratory data is not always standardized across healthcare organizations. Testing methods, instruments, reference ranges, terminology, and reporting practices can vary between laboratories.
When AI models are trained or used across different environments, those differences matter.
The ADLM report identifies data harmonization and standardization as important foundations for developing trustworthy AI in laboratory medicine. When organizations improve the consistency and quality of laboratory data, they create a stronger foundation for AI tools to perform effectively.
For laboratory leaders, this means conversations about AI should begin well before implementation. Organizations need to understand their existing data, how it is generated, and whether their systems are prepared to support new technologies.
2. Validation Needs to Be an Ongoing Process
Laboratory professionals already understand the importance of validation.
Before a new test or process becomes part of routine laboratory operations, teams need confidence that it performs as expected. AI should be approached with a similar level of scrutiny.
An AI system may perform successfully during initial testing but encounter new variables once introduced into a real clinical environment. Patient populations can change. Laboratory methods can change. Instruments can be replaced. Workflows can evolve.
Any of those changes may affect how an AI system performs.
That is why validation should not end once a technology is implemented. Organizations need processes for continuously monitoring performance, identifying unexpected outcomes, and determining when an AI system needs to be reevaluated.
For healthcare leaders, this also creates an important accountability question: Who is responsible for ensuring the technology continues to perform as intended?
Clear ownership and ongoing oversight should be part of the implementation strategy from the beginning.
3. Laboratory Professionals Need a Voice in AI Decisions
AI may be a technology conversation, but it is also a clinical quality conversation.
Laboratory professionals bring valuable experience in areas that are directly relevant to responsible AI implementation, including quality assurance, validation, regulatory compliance, data interpretation, documentation, and performance monitoring.
That expertise should make laboratory leadership an important part of conversations surrounding AI governance.
Technology teams may understand how an AI system operates, but laboratory professionals understand the clinical environment in which that system will be used. They know where data originates, what can influence laboratory results, how workflows operate, and what potential errors could mean for patient care.
Bringing those perspectives together can help healthcare organizations evaluate AI based not only on what the technology can do, but also on how safely and effectively it can be incorporated into existing clinical operations.
4. AI Will Change Skills, Not Eliminate the Need for Expertise
Whenever automation or AI enters the workforce conversation, one question tends to follow: What does this mean for jobs?
For laboratories, a more useful question may be: What skills will the future laboratory workforce need?
As AI handles more data processing, pattern recognition, or repetitive tasks, laboratory professionals may spend more time evaluating outputs, investigating exceptions, overseeing automated systems, managing data quality, and supporting clinical interpretation.
That makes workforce development an important part of AI readiness.
Healthcare organizations may need to provide existing teams with opportunities to build new technical and data-related skills. At the same time, recruiting strategies may need to evolve as laboratories look for professionals capable of working within increasingly technology-enabled environments.
AI can support laboratory professionals, but it cannot replace the clinical judgment, experience, and accountability required to maintain high-quality laboratory operations.
At MedPro Healthcare Staffing, we understand that laboratory transformation ultimately depends on having the right people in place. As technology changes how laboratories operate, healthcare organizations will continue to need qualified professionals who can adapt to new workflows while protecting the quality and reliability at the center of laboratory medicine.
Preparing your laboratory workforce for what comes next? Explore MedPro’s MedTech staffing solutions for laboratories and learn how our short- and long-term staffing solutions can help support your laboratory’s evolving workforce needs.
5. Workforce Planning and Technology Planning Should Happen Together
One of the biggest mistakes healthcare organizations can make is treating technology implementation and workforce planning as separate initiatives.
New technology changes workflows.
When workflows change, staffing needs can change with them.
Before implementing AI or additional automation, laboratory leaders should consider how responsibilities will shift, which skills will become more important, where additional training may be required, and whether current staffing levels provide enough capacity to manage the transition.
This is especially important for laboratories already facing recruitment challenges, retirements, or difficulty filling specialized roles.
Technology may help laboratories become more efficient, but successful transformation still depends on having qualified professionals available to implement, monitor, and optimize those systems.
Preparing the Laboratory for What Comes Next
AI presents significant opportunities for laboratory medicine. It could help laboratories improve efficiency, manage growing volumes of information, identify patterns more quickly, and support better clinical decision-making.
But the laboratories that benefit most will likely be those that look beyond the technology itself.
Strong data practices, continuous validation, clear oversight, workforce development, and laboratory expertise all need to be part of the conversation.
AI may help shape the laboratory of the future.
The laboratory workforce will help determine how successfully we get there.