Evidence map›Paper›PMID 41607824›Full record

ReviewWorld journal of transplantation2026

Protocol for a global electronic Delphi on integrating artificial intelligence into solid organ transplantation.

Rowan Abuyadek, Sara A Ghitani, Ramy Shaaban, Muhammad AbdelAziz Quoritem, Mohammed S Foula, Rodaina Osama Abdel Majid, Manar Mokhtar, Yasir Ahmed Mohammed Elhadi, Amr Alnagar

Abstract readReview
In one paragraph

Review in World journal of transplantation, 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

9 authors.

Rowan AbuyadekDepartment of Health Administration and Behavioural Sciences, High Institute of Public Health, Alexandria University, Alexandria 21561, Egypt.
Sara A GhitaniDepartment of Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Alexandria University, Alexandria 21524, Egypt.
Ramy ShaabanDepartment of Instructional Technology and Learning Sciences, Utah State University, Logan, UT 84322, United States.
Muhammad AbdelAziz QuoritemDepartment of Nephrology, Alsabah Hospital, Farwaneya 00965, Kuwait.
Mohammed S FoulaDepartment of Surgery, King Fahad University Hospital, Imam Abdulrahman Bin Faisal University, Khobar 34443, Saudi Arabia.
Rodaina Osama Abdel MajidDepartment of Public Health, High Institute of Public Health Alexandria, Alexandria University, Alexandria 21531, Al Iskandarīyah, Egypt.
Manar MokhtarDepartment of General Surgery, Royal Glamorgan Hospital, Cwm Taf Morgannwg University Health Board, Rhondda CF72 8XR, Rhondda Cynon Taff, United Kingdom.
Yasir Ahmed Mohammed ElhadiInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates.
Amr AlnagarDepartment of General Surgery, University Hospitals of Birmingham, Birmingham B13 0QB, United Kingdom. amr.alnagar@nhs.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly recognized as a transformative force in the field of solid organ transplantation. From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy, AI has the potential to improve both operational efficiency and patient outcomes. Despite these advancements, the perspectives of transplant professionals - those at the forefront of critical decision-making - remain insufficiently explored. To address this gap, this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation. Participants are invited to complete structured surveys capturing demographic data, professional roles, institutional practices, and prior exposure to AI technologies. The survey also explores perceptions of AI's potential benefits. Quantitative responses are analyzed using descriptive statistics, while open-ended qualitative responses undergo thematic analysis. Preliminary findings indicate a generally positive outlook on AI's role in enhancing transplantation processes, particularly in areas such as donor matching and post-operative care. These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows. By capturing a wide range of expert opinions, the findings will inform future policy development, regulatory considerations, and institutional readiness frameworks for the integration of AI into organ transplantation.

Indexed as

Artificial intelligenceDigital healthDonor matchingElectronic DelphiExpert consensusSolid organ transplantation

Identifiers

PMID41607824
PMCPMC12836247

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.