Authored by Review Committee Chairs – Nadine Lerret Ph.D., MLS(ASCP)^CM – DRC Chair, Phyllis Ingham, EdD MEd MLS (ASCP)^CM AHI(AMT) – PARC Chair, Charity E. Accurso, PhD, MLS(ASCP)^CM – Outgoing DRC Chair

As chairs of the NAACLS Review Committees, we have the unique vantage point of overseeing evolving trends in medical laboratory education. One of the most transformative forces we are witnessing is the integration of artificial intelligence (AI) into both education and accreditation. AI’s presence in healthcare is undeniable, and as educators and accrediting bodies, we must prepare for its implications while ensuring that our standards maintain the rigor and quality expected in clinical laboratory science programs.

AI in Clinical Laboratory Education

AI is reshaping how clinical laboratory students learn, offering adaptive learning platforms, virtual simulations, and real-time data analysis tools. These technologies provide students with personalized learning experiences, helping them grasp complex diagnostic concepts with unprecedented clarity. AI-powered simulations allow students to practice laboratory techniques, analyze case studies, and receive instant feedback—enhancing their readiness for real-world scenarios

Moreover, AI-driven analytics can identify gaps in student performance, enabling educators to tailor instruction more effectively and streamline tasks. However, while AI enhances learning, it cannot replace the critical thinking, hands-on experience, interpersonal skills and collaboration that laboratory professionals require. The challenge for educators is to strike a balance—leveraging AI’s strengths while preserving the human expertise essential in laboratory medicine.

AI’s Influence on Accreditation Standards

From an accreditation perspective, AI presents both opportunities and challenges. AI-driven assessment tools can provide real-time monitoring of student competency, offering data-driven insights into curriculum effectiveness. These tools can help ensure that programs meet NAACLS standards by tracking student progress, identifying areas of concern, and providing objective measures of competency.

However, integrating AI into accreditation also raises concerns. How do we validate the reliability of AI-generated assessments? How do we ensure that AI-driven curriculum enhancements align with established educational standards? A significant challenge in accreditation lies in the lack of consistent institutional policies on AI use. Nationwide, there is substantial variability in how universities address AI adoption. Many institutions have yet to develop clear guidelines, leaving students to independently explore AI technologies, which creates disparities in AI knowledge and preparedness. Programs may wish to monitor institutional policies regarding AI literacy. Institutional support, through proactive AI policy development, can significantly increase responsible AI adoption and reduce educational disparities. Furthermore, while AI can streamline program evaluations, it cannot replace the value of discussions that occur through site visits, faculty and student engagement, program and advisory board meetings, employee and graduate feedback, and the human judgment required in accreditation decisions.

Ethical Considerations and the Human Element

AI’s use in education and accreditation must be approached with caution. Bias in AI algorithms, data privacy concerns, and the potential over-reliance on automated systems all pose challenges. The human element in education and accreditation remains irreplaceable—faculty mentorship, professional judgment, and ethical decision-making are foundational aspects of laboratory science that AI cannot replicate.

As we move forward, NAACLS and its Review Committees must carefully consider how AI aligns with accreditation processes. Guidelines addressing AI’s role should be supportive rather than directive, ensuring that programs produce competent, well-rounded laboratory professionals who are equipped to work alongside AI rather than be replaced by it.  Data privacy compliance with regulations such as FERPA is essential.  Additionally, curriculum design must intentionally reinforce students’ analytical skills to mitigate over-dependence on automated outputs. 

AI Applications in Other Medical Fields

Radiology, pathology and clinical diagnostics highlight how AI enhances medical education and professional practice.  Radiology employs AI for interactive, realistic simulations and personalized learning experiences.  Pathology leverages AI-generated learning materials and digital slide analyses, emphasizing human oversight.  While most clinical laboratories have yet to incorporate AI into their workflow, AI-driven automation has been shifting professionals towards an oversight and interpretation role for some time now, which should be integrated in educational programs. 

Looking Ahead

From where we sit, the future of AI in clinical laboratory education and accreditation is promising but requires thoughtful integration. Educators must be proactive in leveraging AI’s benefits while upholding the core competencies of laboratory medicine. Accreditation bodies must remain vigilant, ensuring that AI enhances rather than dictates the standards we uphold.  Employers across numerous fields are prioritizing job candidates with AI proficiency.  Given the workforce trends, laboratory educators must ensure that their graduates are not only proficient in laboratory techniques but also competent in managing and interpreting AI-generated data and understand the advantages and limitations of the tools. This dual competency will be essential to their employability and success in future healthcare environments, increasingly reliant on AI-driven technologies.

As stewards of clinical laboratory education, we are committed to embracing innovation while safeguarding the integrity of our profession. The challenge ahead is not whether AI has a place in education and accreditation, but how we ensure its role strengthens the foundation upon which our field is built.  Future trends suggest an integral role for AI in education, which means we’ll need strong partnerships between educators and AI specialists to keep these technologies aligned with our teaching goals and ethical standards. Continued assessment and sharing of best practices will ensure AI strengthens the foundational competencies of laboratory medicine without compromising human judgement and oversight. 

References

• American Society for Clinical Laboratory Science. (2024). Artificial intelligence in laboratory medicine. ASCLS Today, 38(2), 12-15. https://ascls.org/artificial-intelligence-in-laboratory-medicine/
• Association to Advance Collegiate Schools of Business. (2024). How can AI support your accreditation efforts? AACSB Insights. https://www.aacsb.edu/insights/articles/2024/09/how-can-ai-support-your-accreditation-efforts
• Fierce Education. (2023). Harnessing the power of AI for quality assurance in education. Fierce Education. https://www.fierceeducation.com/technology/harnessing-power-ai- United Kingdom Accreditation Service. (2024). The role of accreditation in ethical AI governance and innovation. UKAS News. https://www.ukas.com/resources/resources/accreditation-and-ai-governance/
• Khan, A. M., & Javaid, M. A. (2023). Artificial intelligence revolutionizing the field of medical education. International Journal of Medical Education, 14, 123-130. https://doi.org/10.5116/ijme.63f5.2b8a
• Siemens Healthineers. (2023). The evolving role of artificial intelligence in laboratory testing. Siemens Healthineers Webinars. https://www.siemens-healthineers.com/laboratory-diagnostics/assays-by-diseases-conditions/webinars/evolving-role-artificial-intelligence-laboratory-testing
• Belsky L. Building an AI-Ready Workforce: A Look at College Student ChatGPT Adoption in the US. OpenAI; 2025. Available from: (2025.https://openai.com/news/research/).  Accessed March 25.
• Illinois College of Education. (2024). AI in Schools: Pros and Cons. education.illinois.edu
• U.S. Department of Education, Office of Educational Technology. (2023). Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. ed.gov
• Fennelly-Atkinson, R., & Franklin, K. (2023). Can AI be Leveraged to Support Competency-Based Assessments? Digital Promise.
• Center on Reinventing Public Education. (2023). AI is already disrupting education, but only 13 states are offering guidance for schools.
• Digital Pathology Association. (2025). Harnessing the Power of Generative Artificial Intelligence in Pathology Education: Opportunities, Challenges, and Future Directions.
• Frontiers in Public Health. (2024). Physicians’ ethical concerns about artificial intelligence in medicine: “The final decision should rest with a human”.