Logo do repositório
 
Publicação

Revolutionizing Kidney Transplantation

2024-03Recensão Acesso aberto
Ver registo simples
dc.contributor.authorRamalhete, Luís
dc.contributor.authorAlmeida, Paula
dc.contributor.authorFerreira, Raquel
dc.contributor.authorAbade, Olga
dc.contributor.authorTeixeira, Cristiana
dc.contributor.authorAraújo, Rúben
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutioniNOVA4Health - pólo NMS
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2024-04-11T00:36:01Z
dc.date.available2024-04-11T00:36:01Z
dc.date.issued2024-03
dc.descriptionPublisher Copyright: © 2024 by the authors.
dc.description.abstractThis review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can enhance the transplantation process, from donor selection to postoperative patient care. Our methodology involved a comprehensive review of current research, focusing on the application of AI and ML in various stages of KT. This included an analysis of donor–recipient matching, predictive modeling, and the improvement in postoperative care. The results indicated that AI and ML significantly improve the efficiency and success rates of KT. They aid in better donor–recipient matching, reduce organ rejection, and enhance postoperative monitoring and patient care. Predictive modeling, based on extensive data analysis, has been particularly effective in identifying suitable organ matches and anticipating postoperative complications. In conclusion, this review discusses the transformative impact of AI and ML in KT, offering more precise, personalized, and effective healthcare solutions. Their integration into this field addresses critical issues like organ shortages and post-transplant complications. However, the successful application of these technologies requires careful consideration of their ethical, privacy, and training aspects in healthcare settings.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent17
dc.format.extent333260
dc.identifier.doi10.3390/biomedinformatics4010037
dc.identifier.issn2673-7426
dc.identifier.otherPURE: 87106648
dc.identifier.otherPURE UUID: 005a9f61-ec01-4983-ac56-9c8e2c3c8c29
dc.identifier.otherScopus: 85188786172
dc.identifier.urihttp://hdl.handle.net/10362/166077
dc.identifier.urlhttps://www.scopus.com/pages/publications/85188786172
dc.language.isoeng
dc.peerreviewedyes
dc.subjectartificial intelligence
dc.subjectkidney transplantation
dc.subjectmachine learning
dc.subjectprecision medicine
dc.subjectComputer Science (miscellaneous)
dc.subjectMedicine (miscellaneous)
dc.subjectHealth Informatics
dc.subjectHealth Professions (miscellaneous)
dc.titleRevolutionizing Kidney Transplantationen
dc.title.subtitleConnecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allograftsen
dc.typereview
degois.publication.firstPage673
degois.publication.issue1
degois.publication.lastPage689
degois.publication.titleBioMedInformatics
degois.publication.volume4
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
biomedinformatics-04-00037.pdf
Tamanho:
325.45 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NMS: CHRC - Artigos em revista internacional com arbitragem científica