TY - JOUR
T1 - Revolutionizing Kidney Transplantation
T2 - Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts
AU - Ramalhete, Luís
AU - Almeida, Paula
AU - Ferreira, Raquel
AU - Abade, Olga
AU - Teixeira, Cristiana
AU - Araújo, Rúben
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/3
Y1 - 2024/3
N2 - This 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.
AB - This 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.
KW - artificial intelligence
KW - kidney transplantation
KW - machine learning
KW - precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85188786172&partnerID=8YFLogxK
U2 - 10.3390/biomedinformatics4010037
DO - 10.3390/biomedinformatics4010037
M3 - Review article
AN - SCOPUS:85188786172
SN - 2673-7426
VL - 4
SP - 673
EP - 689
JO - BioMedInformatics
JF - BioMedInformatics
IS - 1
ER -