Evidence map›Paper›PMID 42088540›Full record

ArticleTransplant international : official journal of the European Society for Organ Transplantation2026

Shaping the Future of AI in Organ Transplantation: Position Paper of the European Society for Organ Transplantation.

Georgios Kourounis, Stephen Gilbert, Simon R Knight, Amanda Leal, Jackie Leach Scully, Alexandre Loupy, Dominique E Martin, Evgenia Preka, Nadia Primc, Fernando Seoane Martinez and 3 more

Abstract readConsensus Statement
PubMed Full text at PMC Publisher
In one paragraph

Article in Transplant international : official journal of the European Society for Organ Transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Beyond generic principles: a framework for the ethical analysis of artificial intelligence in organ transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2026
    Article
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

13 authors.

Georgios KourounisNIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Newcastle University and Cambridge University, Newcastle upon Tyne, United Kingdom.
Stephen GilbertElse Kröner Fresenius Centre for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Simon R KnightNuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Amanda LealThe Global Agency for Responsible AI in Health, Geneva, Switzerland.
Jackie Leach ScullyDisability Innovation Institute, University of New South Wales, Sydney, NSW, Australia.
Alexandre LoupyUniversité Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Dominique E MartinSchool of Medicine, Deakin University, Geelong, VIC, Australia.
Evgenia PrekaUniversité Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Nadia PrimcInstitute of History and Ethics of Medicine, Medical Faculty, Heidelberg University, Heidelberg, Germany.
Fernando Seoane MartinezDepartment of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
Helena WebbSchool of Computer Science, University of Nottingham, Nottingham, United Kingdom.
Gabriel C OniscuDivision of Transplantation Surgery, CLINTEC, Karolinska Institutet, Stockholm, Sweden.
Colin WilsonNIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Newcastle University and Cambridge University, Newcastle upon Tyne, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in AI hold considerable promise for organ transplantation. While every transformation brings change, not all change is transformative. Despite the rapid growth of AI in medicine, most applications remain in developmental or experimental stages, with relatively few having been successfully integrated into routine clinical practice. As a professional society, ESOT recognises that achieving meaningful impact will require more than technical progress. This position paper outlines five critical domains for successful implementation. (1) High-quality development: Coordinated collaboration and methodological rigour are prerequisites for trust; AI is only as robust as the data used to train it. (2) Ethical considerations: We must address risks to equity and access to care, and move from generic ethical principles to transplantation-specific ethical guidance. (3) Regulatory landscape: AI in transplantation is regulated under both EU medical device and AI legislation; compliance is central to stakeholder trust. (4) Responsible adoption: AI should augment, not replace, human expertise. Strengthening AI literacy is essential for meaningful adoption. (5) Participatory design: Active involvement of transplant professionals and patients is essential to address real clinical needs. These statements serve as a strategic framework to guide clinicians, researchers, and policymakers in making AI a genuine force multiplier for the transplant community.

Indexed as

Artificial IntelligenceOrgan TransplantationEuropeHumansSocieties, Medicalartificial intelligence (AI)ESOTmachine learningorgan transplantation (OT)position paper

Identifiers

PMID42088540
PMCPMC13136039

What OpenQuestion holds

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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 OpenQuestion graph.