Evidence map›Paper›PMID 42684470›Full record

ReviewVirchows Archiv : an international journal of pathology2026

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

Emad A Rakha, Jelle Wesseling, Anikó Kovács, Aleš Ryška, Elena Provenzano, Gábor Cserni, Zsuzsanna Varga, Zsuzsanna Bagó-Horváth, Emmanuelle Charafe-Jauffret, Janina Kulka and 6 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Virchows Archiv : an international journal of pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Emad A RakhaSchool of Medicine, University of Nottingham, Nottingham, UK. erakha@nrl.ae.
Jelle WesselingDepartment of Pathology and Division of Molecular Pathology, Centre for Early Cancer Detection, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands.
Anikó KovácsDepartment of Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Aleš RyškaThe Fingerland Department of Pathology, Faculty of Medicine and University Hospital, Charles University, Hradec Králové, Czech Republic.
Elena ProvenzanoDepartment of Histopathology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Gábor CserniDepartment of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
Zsuzsanna VargaDepartment of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland.
Zsuzsanna Bagó-HorváthDepartment of Pathology, Medical University of Vienna, Vienna, Austria.
Emmanuelle Charafe-JauffretDepartment of Pathology, Institut Paoli-Calmettes and Aix-Marseille Université, Marseille, France.
Janina KulkaDepartment of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary.
Carolien H M van DeurzenDepartment of Pathology and Bioinformatics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands.
Antonio PolóniaSchool of Medicine and Biomedical Sciences, Fernando Pessoa University, Porto, Portugal.
Thomas DeckerReference Centre for Mammography, University Hospital Münster, Münster, Germany.
Paul J van DiestDepartment of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands.
Cecily QuinnIrish National Breast Screening Programme, Dublin, Ireland.
European Working Group for Breast Screening Pathology (EWGBSP).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceClinical governance and quality assuranceDigital pathologyHistopathologyLaboratory implementationLocal verification

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.