Guidance for laboratory implementation, governance and continuous assurance of artificial intelligence in histopathology

Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin

Abstract

Artificial intelligence (AI) is rapidly transforming histopathology, with applications ranging from workflow optimisation and quality assurance to tumour diagnosis, grading, biomarker assessment and estimation of prognosis. While numerous AI algorithms have demonstrated promising analytical and clinical performance, pathology laboratories are increasingly adopting commercially available AI systems with regulatory-approval rather than developing their own algorithms. Existing guidance largely focuses on AI development, validation and regulatory approval, with comparatively little practical direction on the local verification, governance and ongoing assurance required for safe routine clinical implementation. This paper proposes a practical framework for the clinical implementation of AI specifically within pathology laboratories. Rather than addressing AI development, it focuses on the responsibilities of laboratories adopting established AI systems into clinical practice. The framework distinguishes AI applications according to their intended clinical function, recognising that diagnostic applications, biomarker evaluation, workflow optimisation and generative AI applications require different implementation, verification, governance and quality assurance strategies. It further distinguishes algorithm validation, local verification and continuous assurance as complementary stages of implementation and advocates a function-based, risk-proportionate approach integrated within existing laboratory quality management systems. Practical recommendations are provided for workflow integration, interoperability, human oversight, user competency, performance monitoring, incident management, software updates and proportionate re-verification throughout the AI operational lifecycle. By extending implementation beyond regulatory approval, this guidance complements existing AI development and regulatory frameworks rather than replacing them. It provides a practical governance framework for pathology laboratories, professional organisations, accreditation bodies, and healthcare providers to support the safe, standardised, and sustainable integration of AI into routine histopathology while maintaining diagnostic quality, patient safety, and clinical governance.

Authors 16

1. Emad A. Rakha corresponding

   University of Nottingham · Cleveland Clinic Abu Dhabi

   Affiliation as printed

   National Reference Laboratory and Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates. erakha@nrl.ae

   School of Medicine, University of Nottingham, Nottingham, UK. erakha@nrl.ae

2. Jelle Wesseling Aachen Department of Pathology, Leiden University Medical Center

   Leiden University Medical Center · The Netherlands Cancer Institute

   Affiliation as printed

   Department of Pathology and Division of Molecular Pathology, Centre for Early Cancer Detection, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands

   Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands

3. Anikó Kovács

   Sahlgrenska University Hospital

   Affiliation as printed

   Department of Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden

4. Aleš Ryška

   Charles University

   Affiliation as printed

   The Fingerland Department of Pathology, Faculty of Medicine and University Hospital, Charles University, Hradec Králové, Czech Republic

5. Elena Provenzano

   Cambridge University Hospitals NHS Foundation Trust · Addenbrooke's Hospital

   Affiliation as printed

   Department of Histopathology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

6. Gábor Cserni

   University of Szeged · Bács-Kiskun Megyei Kórház

   Affiliation as printed

   Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary

   Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary

7. Zsuzsanna Varga

   University Hospital Zurich

   Affiliation as printed

   Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland

8. Zsuzsanna Bagó-Horváth

   Medical University of Vienna

   Affiliation as printed

   Department of Pathology, Medical University of Vienna, Vienna, Austria

9. Emmanuelle Charafe‐Jauffret

   Aix-Marseille Université · Institut Paoli-Calmettes

   Affiliation as printed

   Department of Pathology, Institut Paoli-Calmettes and Aix-Marseille Université, Marseille, France

10. Janina Kulka

    Semmelweis University

    Affiliation as printed

    Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary

11. Carolien H. M. van Deurzen

    Erasmus MC · Erasmus MC Cancer Institute

    Affiliation as printed

    Department of Pathology and Bioinformatics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands

12. António Polónia

    Fernando Pessoa University

    Affiliation as printed

    School of Medicine and Biomedical Sciences, Fernando Pessoa University, Porto, Portugal

13. Thomas Decker

    University Hospital Münster

    Affiliation as printed

    Reference Centre for Mammography, University Hospital Münster, Münster, Germany

14. Paul J. van Diest

    University Medical Center Utrecht

    Affiliation as printed

    Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands

15. Cecily Quinn

    University College Dublin · St. Vincent's University Hospital

    Affiliation as printed

    Department of Histopathology, St Vincent's University Hospital and School of Medicine, University College Dublin, Dublin, Ireland

    Irish National Breast Screening Programme, Dublin, Ireland

16. On behalf of the European Working Group for Breast Screening Pathology (EWGBSP).