From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2026
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| _version_ | 1867006977116209152 |
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| author | Maoxin Liao Cheng Yang |
| author_facet | Maoxin Liao Cheng Yang Maoxin Liao Cheng Yang |
| collection | Wiley Open Access |
| contents | From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation Maoxin Liao Cheng Yang Organ Medicine ABSTRACT This review discusses the potential applications of machine learning, multimodal data integration, and generative artificial intelligence (AI) in the field of kidney transplantation. Effective prediction of allograft survival, postoperative complications, and rejection after kidney transplantation remains a central research challenge. Machine learning has shown considerable potential in predicting post‐transplant outcomes by analyzing a large amount of clinical data and images. Multimodal data integration improves the accuracy of predictive models by fusing multimodal data from different sources, such as genomic, imaging, and clinical, to support personalized treatment. Generative AI builds upon both of these approaches. Although still in its early stages, generative AI shows great potential for data augmentation, disease simulation, personalized prediction, and education and training. However, the application of these technologies still faces many challenges, such as data insufficiency, limited model generalizability, as well as ethical and regulatory concerns. In the future, further technological innovations and multidisciplinary collaborations are needed to promote the widespread use of these techniques in kidney transplantation. 10.1002/orm2.70039 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/orm2.70039 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_orm2_70039 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation Maoxin Liao Cheng Yang Organ Medicine From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation Maoxin Liao Cheng Yang Organ Medicine ABSTRACT This review discusses the potential applications of machine learning, multimodal data integration, and generative artificial intelligence (AI) in the field of kidney transplantation. Effective prediction of allograft survival, postoperative complications, and rejection after kidney transplantation remains a central research challenge. Machine learning has shown considerable potential in predicting post‐transplant outcomes by analyzing a large amount of clinical data and images. Multimodal data integration improves the accuracy of predictive models by fusing multimodal data from different sources, such as genomic, imaging, and clinical, to support personalized treatment. Generative AI builds upon both of these approaches. Although still in its early stages, generative AI shows great potential for data augmentation, disease simulation, personalized prediction, and education and training. However, the application of these technologies still faces many challenges, such as data insufficiency, limited model generalizability, as well as ethical and regulatory concerns. In the future, further technological innovations and multidisciplinary collaborations are needed to promote the widespread use of these techniques in kidney transplantation. 10.1002/orm2.70039 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation |
| topic | Organ Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/orm2.70039 |