Practical consequences of the European union-AI act for anatomic pathology laboratories a European society of pathology and European society of digital and integrative pathology commissioned expert opinion paper
The European Union Artificial Intelligence Act (AI Act) introduces a landmark regulatory framework governing the development, deployment, and post-marketing requirements and maintenance of AI systems, with growing relevance for the field of digital pathology. This paper explores the practical implications of the AI Act for pathology laboratories, particularly in relation to high-risk AI tools used in diagnostic workflows. We outline the Act's risk-based classification approach and highlight key obligations for both AI developers and users, including requirements for transparency, explainability, risk management, data governance, human oversight, and staff training. Special attention is given to how these regulatory demands relate to existing healthcare standards and implemented quality systems in anatomic pathology laboratories. By translating the AI Act's legal language into concrete, pathology-specific recommendations, this work provides guidance supporting safe and effective AI integration in clinical practice. While the legislation introduces operational and administrative challenges, it also presents an opportunity to enhance accountability, trust, and innovation in pathology. This paper aims to equip anatomic pathology laboratories with the tools and insights needed to responsibly navigate the evolving regulatory landscape of AI in healthcare.
Authors
- Luca Di Tommaso (ORCID: https://orcid.org/0000-0002-9013-4728)
- Vincenzo L’Imperio (ORCID: https://orcid.org/0000-0002-9284-2998)
- Anouk Waeytens (ORCID: https://orcid.org/0000-0002-5125-9536)
- Sofia Palmieri (ORCID: https://orcid.org/0000-0002-6618-5963)
- Glenn Broeckx (ORCID: https://orcid.org/0000-0002-7543-8061)
- Sara P. Oliveira (ORCID: https://orcid.org/0000-0002-6586-9079)
- Thomas Van Den Berghe (ORCID: https://orcid.org/0000-0002-0038-8055)
- Amélie Dendooven (ORCID: https://orcid.org/0000-0002-9900-7952)
- Frederik Deman (ORCID: https://orcid.org/0000-0002-6925-571X)
- Inti Zlobec (ORCID: https://orcid.org/0000-0001-6741-3000)
- Norman Zerbe (ORCID: https://orcid.org/0000-0002-0314-3037)
- Roberto Salgado
- Marc Van den Bulcke (ORCID: https://orcid.org/0000-0002-1866-154X)
- Peter Schirmacher
- Sabine Declercq
Institutions
- University of Bern (CH)
- Pathfinder International (US)
- Bern University of Applied Sciences (CH)
- Humanitas University (IT)
- University of Antwerp (BE)
- Heidelberg University (DE)
- Dutch Cancer Society (NL)
- Peter MacCallum Cancer Centre (AU)
- Ghent University Hospital (BE)
- European Society of Cardiology (FR)
- Ghent University (BE)
- Humboldt-Universität zu Berlin (DE)
- European Society of Intensive Care Medicine (BE)
- Azienda Ospedaliera San Gerardo (IT)
- Universitätsklinikum Aachen (DE)
- Heidelberg Engineering (Germany) (DE)
- Sciensano (Belgium) (BE)
- Ziekenhuisnetwerk Antwerpen Stuivenberg (BE)
- IRCCS Humanitas Research Hospital (IT)
- University of Milano-Bicocca (IT)
- Heidelberg University (US)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin
- Published
- 2025-10-16
- DOI
- https://doi.org/10.1007/s00428-025-04291-3
- Citations
- 4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 9.92
Funders
- Université du Luxembourg
- Università di Bologna
- Università degli Studi di Torino