Darryl Elzie, PsyD, MHA, MLS (ASCP), CQA (ASQ)
Amari Henderson, MBA, MLS (ASCP)

Current Applications of Artificial Intelligence in the Clinical Laboratory

Artificial intelligence (AI) has transitioned from an experimental concept to an operational reality in the clinical laboratory. Unlike traditional rule-based automation, AI systems utilize machine learning techniques to analyze data gathered from historical records, identifying patterns, detecting anomalies, and supporting clinical and operational decision-making (Dodig, 2025; Yahya, 2025). The primary goal of laboratory AI applications is to function as decision-support tools, augmenting professional judgment.

At-a-Glance: Where AI Fits in the Clinical Laboratory

In the pre-analytical phase, AI tools are used to flag duplicate or inappropriate orders (an area of challenge for some medical providers), predict specimen transport delays, and optimize collection workflows. These systems are designed to reduce redraws and delays; however, they remain dependent on standardized front-end processes and human beings. AI cannot compensate for poor specimen identification or inconsistent acceptance criteria.

Analytical applications represent one of the fastest-growing areas of AI adoption, especially in quality control. Machine learning models analyze multivariate QC data to identify subtle analyzer drift or reagent shifts that may precede traditional QC rule violations. In hematology, microbiology, and pathology, AI-driven image analysis supports cell classification, culture plate reading, and slide triage (Hanna et al., 2025; Sabelli, 2023).

Analytical applications represent one of the fastest-growing areas of AI adoption, especially in quality control. Machine learning models analyze multivariate QC data to identify subtle analyzer drift or reagent shifts that may precede traditional QC rule violations. In hematology, microbiology, and pathology, AI-driven image analysis supports cell classification, culture plate reading, and slide triage (Hanna et al., 2025; Sabelli, 2023).

Post-analytical AI focuses on consistency and safety. Enhanced delta-checking models incorporate patient-specific baselines, while probabilistic autoverification tools assist in prioritizing results requiring human review. These systems remain bounded by laboratory policy and regulatory expectations for oversight (FDA, 2025).

Governance, Risk, and the Human Side of AI Adoption

Despite measurable benefits, AI adoption introduces new categories of risk. Machine learning models reflect the data on which they are trained and may perpetuate bias related to patient populations, specimen handling practices, or historical testing patterns (NIST, 2023). Models are susceptible to cultural and systemic biases. Performance may degrade over time due to workflow changes or shifts in patient demographics, necessitating ongoing monitoring and reevaluation.

Operational tradeoffs must also be acknowledged. AI systems require integration with laboratory information systems, additional validation effort, and formal change control. While AI may reduce manual review, it often redistributes workload toward exception management, monitoring, and
documentation rather than eliminating labor entirely. It is incumbent on organizations to have adequately trained staff in problem-solving and root cause analysis.

Equally important are the psychological impacts on laboratory professionals. Established change theories, such as Lewin’s Change Model and Kotter’s Eight-Step Process, demonstrate that uncertainty and perceived loss of control are primary drivers of resistance. Laboratory staff may fear deskilling (a demonstrable risk in pathology), increased surveillance, or erosion of professional autonomy if AI is poorly introduced.

Prior scholarship on reshaping organizational culture in pathology emphasizes that sustainable change is rooted in trust, psychological safety, and alignment between stated values and operational behavior (Elzie, 2025). The adoption of artificial intelligence follows these same principles. When laboratory professionals understand how decisions are generated, retain authority to override AI outputs, and observe leadership modeling responsible and ethical use, technology functions as an enabler of professional practice rather than a perceived threat.

Successful laboratories frame AI as a tool automating low-value, repetitive tasks and enhancing professional judgment. Transparent communication, structured training, and visible leadership engagement help normalize AI as part of continuous improvement rather than disruptive replacement.

Ultimately, AI should be governed like any other critical laboratory process: with a clearly defined intended use, risk assessment, validation, change control, and ongoing performance monitoring. When aligned with a strong organizational culture, AI can enhance both operational resilience and professional satisfaction.

References

Alhamzi Nasser Yahya. Artificial 1Intelligence (AI) Applications in Clinical Laboratories: A Narrative Review. Biomed J Sci & Tech Res 62(5)-2025. BJSTR. MS.ID.009813.

Dodig, S. (2025). Are we ready to integrate advanced artificial intelligence models into clinical laboratories? Biochemia Medica, 35(1), 010501.

Elzie, D. (2025). Reshaping organizational culture in pathology. Clinics in Laboratory Medicine, 45(3), 383–396. https://pubmed.ncbi.nlm.nih.gov/40818874/

Food and Drug Administration. (2025). Artificial intelligence in software as a medical device (SaMD). U.S. Food and Drug Administration.

Hanna, M. G., et al. (2025). Future of artificial intelligence—Machine learning trends in pathology and medicine. Modern Pathology.

National Institute of Standards and Technology. (2023). AI risk management framework (AI RMF 1.0). National Institute of Standards and Technology.

Sabelli, Chiara. (2023). An AI system helps microbiologists identify bacteria. Nature Italy. 10.1038/d43978-023-00167-9.