What changes for the laboratory worker with the arrival of AI?

Artificial intelligence (AI) is increasingly finding its way into the laboratory. While automation was long focused primarily on equipment and logistics, the use of AI is now shifting towards analysis, interpretation, and decision support. This development has direct consequences for the daily work of laboratory staff and for the skills required of them.

Traditionally, the emphasis in the laboratory lay on performing measurements, manually checking results, and following established protocols. AI systems are now taking over some of these tasks, for example by automatically recognizing patterns in large datasets or signaling anomalies in measurement results.[1]

For laboratory staff, this does not mean a loss of relevance, but rather a shift in duties. The focus will shift more towards interpretation, assessment, and context. Human expertise remains necessary to validate AI outcomes, identify exceptions, and place results within the clinical or research context.[2]

Concrete applications in practice

AI is already being applied in various places within the laboratory. In clinical laboratories, algorithms support the interpretation of complex test results and the early detection of trends. In pathology and microbiology, image recognition is used for classification and quantification, with AI performing an initial analysis before a specialist makes a final assessment.[3]

AI also plays a role outside of primary analysis, for example in workflow optimization, predictive equipment maintenance, and quality assurance. These applications contribute to more efficient processes and shorter lead times.[4]

Changing skills and roles

With the deployment of AI, the competency profile of laboratory staff is also changing. In addition to subject-matter expertise, digital skills are becoming more important, such as understanding data flows, insight into the functioning and limitations of algorithms, and critically evaluating automated results.[5]

International research shows that AI primarily leads to job enrichment and not to large-scale job replacement. However, there is a growing need for further training and for employees who can act as a link between laboratory practice, IT, and quality management.[6]

Opportunities and points of attention

AI offers clear benefits: higher efficiency, greater consistency of results, and a reduction in routine work. At the same time, there are important points of concern. AI systems are highly dependent on data quality and require careful validation before they can be reliably deployed.[7]

In addition, ethical and organizational issues play a role, such as transparency of algorithms, the allocation of responsibility, and data protection. In regulated environments, human control remains essential to ensure quality and safety.[8]

The laboratory worker of the future

AI is fundamentally changing the laboratory, but not at the expense of the professional. The work is shifting towards interpretation, quality assurance, and collaboration with other disciplines. Laboratory staff who continue to develop and view AI as a supporting tool strengthen their position within the field.[9]

[1] Roche Diagnostics. (2025). How AI and machine learning are revolutionizing the laboratory.
https://diagnostics.roche.com/global/en/lab-leaders/article/machine-learning-ai-in-laboratory.html

[2] Haymond, S., & McCudden, C. (2021). Rise of the machines: Artificial intelligence and the clinical laboratory.
The Journal of Applied Laboratory Medicine, 6(6), 1640–1654.
https://academic.oup.com/jalm/article/6/6/1640/6348035

[3] Zhang, X.‑M., et al. (2026). Artificial intelligence in digital pathology diagnosis and analysis: Technologies, challenges, and future prospects.
Military Medical Research, 12, Article 93.
https://link.springer.com/article/10.1186/s40779-025-00680-6

[4] Lee, R. (2025). Laboratory automation and AI in the modern lab era.
Lab Manager.
https://www.labmanager.com/laboratory-automation-and-ai-in-the-modern-lab-era-34704

[5] Organization for Economic Co‑operation and Development (OECD). (2024). Artificial intelligence and the health workforce: Perspectives from medical associations on AI and health.
https://oecd.ai/en/ai-publications/artificial-intelligence-and-the-health-workforce-perspectives-from-medical-associations-on-ai-and-health

[6] American Society for Clinical Pathology (ASCP). (2025). AI, staffing pressures, and a shifting workforce: Inside ASCP's 2024 vacancy survey.
https://www.ascp.org/news/news-details/2025/12/02/ai–staffing-pressures–and-a-shifting-workforce–inside-ascp-s-2024-vacancy-survey

[7] Ul Ain, Q., et al. (2024). Machine learning approach towards quality assurance: Challenges and possible strategies in laboratory medicine.
Journal of Clinical and Translational Pathology, 4(2), 76–87.
https://www.xiahepublishing.com/2771-165X/JCTP-2023-00061

[8] Sapio Sciences. (2026). Navigating ethical concerns around AI in scientific laboratories.
https://www.sapiosciences.com/blog/navigating-ethical-concerns-around-ai-in-scientific-laboratories/

[9] Organization for Economic Co‑operation and Development (OECD). (2024). Artificial intelligence and the health workforce: Perspectives from medical associations on AI and health.
https://oecd.ai/en/ai-publications/artificial-intelligence-and-the-health-workforce-perspectives-from-medical-associations-on-ai-and-health and American Society for Clinical Pathology (ASCP). (2025). AI, staffing pressures, and a shifting workforce: Inside ASCP's 2024 vacancy survey.
https://www.ascp.org/news/news-details/2025/12/02/ai–staffing-pressures–and-a-shifting-workforce–inside-ascp-s-2024-vacancy-survey