ReviewWorld journal of transplantation2026
ChatGPT in liver transplantation: Current applications, limitations, and future directions.
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. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Pathophysiological mechanisms of cell death affecting graft survival in liver transplantation.World journal of transplantation · 2026Review
- 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 · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver transplantation (LT) remains the optimal life-saving intervention for patients with end-stage liver disease. Despite the recent advances in LT several barriers, including organ allocation, donor-recipient matching, and patient education, persist. With the growing progress of artificial intelligence, particularly large language models (LLMs) like ChatGPT, new applications have emerged in the field of LT. Current studies demonstrating usage of ChatGPT in LT include various areas of application, from clinical settings to research and education. ChatGPT usage can benefit both healthcare professionals, by decreasing the time spent on non-clinical work, but also LT recipients by providing accurate information. Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork. Additionally, the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability. Although ChatGPT usage presents promising applications, there are certain ethical and practical limitations. Key concerns include patient data privacy, information accuracy, misinformation possibility and lack of legal framework. Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT. The aim of this minireview is to summarize current literature on ChatGPT in LT, highlighting both opportunities and limitations, while also providing future possible applications.
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Identifiers
What OpenQuestion holds
Registered trials
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.