Evidence map›Paper›PMID 41357649›Full record

ReviewWorld journal of gastrointestinal surgery2025

Artificial intelligence in liver transplantation: Opportunities and challenges.

Sanjay Goja, Sanjay Kumar Yadav

Abstract readReview
In one paragraph

Review in World journal of gastrointestinal surgery, 2025. 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

2 authors.

Sanjay GojaLiver Transplantation and HPB Surgery, Narayana Health Institute of HPB Surgery and Transplantation, Delhi NCR 122002, Harayana, India. drsanjaygoja@gmail.com.
Sanjay Kumar YadavLiver Transplantation and HPB Surgery, Narayana Health Institute of HPB Surgery and Transplantation, Delhi NCR 122002, Harayana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The management of liver transplant recipients and their outcome prediction is complex due to nonlinear interaction of multiple pre, peri and postoperative factors. Artificial intelligence (AI) has a potentially significant role in understanding and decision making at all stages of liver transplantation procedure. The role starts right from diagnosis of liver cirrhosis, followed by best course of action and prognostication. By analyzing numerous data points, AI can assist in the complex decision-making process of determining transplant candidacy. AI algorithms can analyze vast datasets of donor and recipient characteristics to improve the accuracy of matching, leading to better graft survival rates. This will help in optimizing the allocation of scarce organs, ensuring that they go to the most suitable recipients. AI can be used to predict the pre-operative risk factors and risk of post-operative complications such as graft rejection or post-transplant infections, allowing timely and tailored treatment. AI-powered imaging analysis can assist surgeons in preoperative planning and provide real-time guidance during surgery, increasing precision and improved safety. Therefore, AI has the potential to enhance long term patient and graft survival. The major challenges on use of AI are data availability, data quality, ethical considerations and clinical integration. In essence, AI holds great promise for revolutionizing liver transplantation albeit with some challenges.

Indexed as

Artificial intelligenceCirrhosisDeep learningLiver transplantationMachine learningNeural networksOrgan allocation

Identifiers

PMID41357649
PMCPMC12678989

What OpenQuestion holds

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LicenceCC BY-NC
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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.