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Peer-reviewedCC BYPLOS One

The applications of artificial intelligence in liver transplantation: A comprehensive scoping review

  • Aditya Borakati1,2
  • Joshua Agilinko3
  • Jack Wu4
  • Samora Hunter5
  • Anwar Alhaq5
  • Farid Froghi6
  • James Teo5
  • William Bernal7
  • Chris Callaghan8
  • Miriam Cortes-Cerisuelo1,2
  • David Wallace1,9,10
10 affiliations
  1. 1Roger Williams Institute of Liver Studies, King's College London, London, United Kingdom.
  2. 2Department of Liver Transplantation and Hepatopancreatobiliary Surgery, King's College Hospital, London, United Kingdom.
  3. 3Department of Endocrine Surgery, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom.
  4. 4School of Cardiovascular & Metabolic Medicine and Sciences, King's College London, London, United Kingdom.
  5. 5CogStack, Precision Medicine, Networked Services, King's College Hospital NHS Foundation Trust, London, United Kingdom.
  6. 6Department of Hepatopancreatobiliary Surgery, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom.
  7. 7Liver Intensive Therapy Unit, King's College Hospital, London, United Kingdom.
  8. 8Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
  9. 9Department of Hepatopancreatobiliary Surgery and Liver Transplantation, Royal Free Hospital, Royal Free London NHS Foundation Trust, London, United Kingdom.
  10. 10London School of Hygiene and Tropical Medicine, London, United Kingdom.
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Abstract

Liver transplantation (LT) is a complex surgical intervention, with intricate decision-making and risks at all stages before, during and after the procedure itself. Artificial intelligence (AI) may assist the process but has seen limited use. This scoping review assessed primary articles reporting on clinical AI in LT in MEDLINE and Embase from 2000 to September 2026. 196 articles were identified: focusing on the pre-operative period before transplant (21.9%), organ retrieval (9.7%), intra-operative issues (3.6%), short-term post-operative complications (16.8%), long-term post-operative complications (45.9%) and natural language processing (2.0%). 95.9% had retrospective designs and only 12.8% had direct multi-centre data, with the rest from national registries or single centres. 13.8% of studies externally validated their AI tools. Only one study performed a prospective evaluation of their tool with a randomized controlled trial. Progress towards clinically deployable tools will require harmonised, higher-granularity datasets, consistent reporting, and prospective evaluation within real-world transplant workflows.

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Cite

BibTeXRIS
BibTeX
@article{borakati2026applications,
  title = {{The applications of artificial intelligence in liver transplantation: A comprehensive scoping review}},
  author = {Aditya Borakati and Joshua Agilinko and Jack Wu and Samora Hunter and Anwar Alhaq and Farid Froghi and James Teo and William Bernal and Chris Callaghan and Miriam Cortes-Cerisuelo and David Wallace},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0361120},
  url = {https://doi.org/10.1371/journal.pone.0361120},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - The applications of artificial intelligence in liver transplantation: A comprehensive scoping review
AU  - Aditya Borakati
AU  - Joshua Agilinko
AU  - Jack Wu
AU  - Samora Hunter
AU  - Anwar Alhaq
AU  - Farid Froghi
AU  - James Teo
AU  - William Bernal
AU  - Chris Callaghan
AU  - Miriam Cortes-Cerisuelo
AU  - David Wallace
PY  - 2026
DA  - 2026/10/23
JO  - PLOS One
DO  - 10.1371/journal.pone.0361120
UR  - https://doi.org/10.1371/journal.pone.0361120
AB  - Liver transplantation (LT) is a complex surgical intervention, with intricate decision-making and risks at all stages before, during and after the procedure itself. Artificial intelligence (AI) may assist the process but has seen limited use. This scoping review assessed primary articles reporting on clinical AI in LT in MEDLINE and Embase from 2000 to September 2026. 196 articles were identified: focusing on the pre-operative period before transplant (21.9%), organ retrieval (9.7%), intra-operative issues (3.6%), short-term post-operative complications (16.8%), long-term post-operative complications (45.9%) and natural language processing (2.0%). 95.9% had retrospective designs and only 12.8% had direct multi-centre data, with the rest from national registries or single centres. 13.8% of studies externally validated their AI tools. Only one study performed a prospective evaluation of their tool with a randomized controlled trial. Progress towards clinically deployable tools will require harmonised, higher-granularity datasets, consistent reporting, and prospective evaluation within real-world transplant workflows.
SN  - 1932-6203
ER  -