ReviewPrzeglad gastroenterologiczny2026
Artificial intelligence in liver transplantation: current state, future prospects, and challenges - a narrative review.
Review in Przeglad gastroenterologiczny, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence (AI) in liver transplantation (LT) presents new opportunities to improve safety, outcomes, and efficiency of LT. This review explores AI's potential applications, including enhanced donor-recipient matching, predictive analytics for post-transplant outcomes, organ viability assessment, and organisational benefits for surgeons. AI's ability to compile and analyse complex datasets can optimise donor liver utilisation and personalise immunosuppressive therapies. Furthermore, AI-driven systems can improve logistical management and postoperative monitoring, enabling early detection of complications. Implementation of various AI systems in everyday practice could result in higher success rates of LT and better patient care. This narrative minireview highlights current advancements, challenges, and future directions for AI in LT.
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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.