Advances in the application of artificial intelligence in kidney transplantation

doi:10.3877/cma.j.issn.1674-3903.2025.06.017

Abstract

Abstract:

With the development of technology and the informatization of medical big data, artificial intelligence (AI) and machine learning have been increasingly applied in the medical field. With continuous improvements in surgical techniques of kidney transplantation and the progression towards individualized and refined perioperative and postoperative management, quality of life and long-term prognosis have significantly improved. AI technologies are gradually transforming traditional practice in kidney transplantation management. This article comprehensively analyzes the latest research findings on the application of AI in kidney transplantation in the areas of donor-recipient matching, surgical assistance, prediction of postoperative complications, and immunosuppressant management both domestically and internationally. The aim is to promote further research and facilitate the application of AI in the field of kidney transplantation, ultimately improving outcomes for more kidney transplant patients.

Key words: Kidney transplantation, Artificial intelligence, Renal transplant matching, Transplant surgery, Immunosuppression

Boqin Li, Yichen Zhu, Ye Tian, Meishan Zhao. Advances in the application of artificial intelligence in kidney transplantation[J]. Chinese Journal of Transplantation(Electronic Edition), 2025, 19(06): 482-485.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhyzzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-3903.2025.06.017

https://zhyzzz.cma-cmc.com.cn/EN/Y2025/V19/I06/482

References 21

1	Badrouchi S, Bacha MM, Hedri H, et al. Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation[J]. J Nephrol, 2023，36(4)：1087-1100.
2	周小芹，刘慧珍，王婷，等. 人工智能赋能医学领域的挑战与发展方向[J]. 中国胸心血管外科临床杂志，2025，32(2)：244-251.
3	王燕萍，金钢，赵佳，等. 我国医疗人工智能的应用现状、风险及对策建议[J]. 卫生软科学，2024，38(10)：74-78.
4	Kotsifa E, Mavroeidis VK. Present and future applications of artificial intelligence in kidney transplantation[J]. J Clin Med, 2024，13(19)：5939.
5	Paquette FX, Ghassemi A, Bukhtiyarova O, et al. Machine learning support for decision-making in kidney transplantation: step-by-step development of a technological solution[J]. JMIR Med Inform, 2022，10(6)：e34554.
6	Ali H, Mohamed M, Molnar MZ, et la. Deceased-donor kidney transplant outcome prediction using artificial intelligence to aid decision-making in kidney allocation[J]. ASAIO J, 2024，70(9)：808-818.
7	Bae S, Massie AB, Thomas AG, et al. Who can tolerate a marginal kidney? Predicting survival after deceased donor kidney transplant by donor-recipient combination[J]. Am J Transplant, 2019，19(2)：425-433.
8	Knudsen JE, Ghaffar U, Ma R, et al. Clinical applications of artificial intelligence in robotic surgery[J]. J Robot Surg, 2024，18(1)：102.
9	Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions[J]. Nat Biomed Eng, 2017，1(9)：691-696.
10	Piana A, Gallioli A, Amparore D, et al. Three-dimensional augmented reality-guided robotic-assisted kidney transplantation: breaking the limit of atheromatic plaques[J]. Eur Urol, 2022，82(4)：419-426.
11	De Backer P, Van Praet C, Simoens J, et al. Improving augmented reality through deep learning: real-time instrument delineation in robotic renal surgery[J]. Eur Urol, 2023，84(1)：86-91.
12	中华医学会器官移植学分会，中国医师协会器官移植医师分会. 中国肾移植排斥反应临床诊疗指南(2016版)[J]. 器官移植，2016，7(5)：332-338.
13	Jo SJ, Park JB, Lee KW. Prediction of very early subclinical rejection with machine learning in kidney transplantation[J]. Sci Rep, 2023，13(1)：22387.
14	白云昊，宋相钦，余一凡，等. 肾移植术后肺部感染的危险因素和临床诊疗现状[J]. 重庆医学，2025，54(2)：516-520.
15	Peng B, Gong H, Tian H, et al. The study of the association between immune monitoring and pneumonia in kidney transplant recipients through machine learning models[J]. J Transl Med, 2020，18(1)：370.
16	Wu X, Zhang H, Cai M, et al. Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study[J]. Int Urol Nephrol, 2025，57(3)：947-955.
17	Burghelea D, Moisoiu T, Ivan C, et al. The use of metabolomics and machine learning algorithms to predict post-transplant diabetes mellitus in renal transplant patients on Tacrolimus therapy[J]. Med Pharm Rep, 2024，97(4)：467-476.
18	Kamal J, Doyle A. Immunosuppression and kidney transplantation[J]. Handb Exp Pharmacol, 2022，272：165-179.
19	Tang J, Liu R, Zhang YL, et al. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients[J]. Sci Rep, 2017，7：42192.
20	Niel O, Bastard P. Artificial intelligence improves estimation of tacrolimus area under the concentration over time curve in renal transplant recipients[J]. Transpl Int, 2018，31(8)：940-941.
21	Woillard JB, Labriffe M, Debord J, et al. Mycophenolic acid exposure prediction using machine learning[J]. Clin Pharmacol Ther, 2021，110(2)：370-379.

[1]	Zhuyu Wang, Jingjing Quan. Artificial intelligence-assisted basic and clinical research in endodontics [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 25-33.
[2]	Siyi Lü, Yanqi Wang, Jun Qiu, Yujiang Chen, Jie Gao. Application of artificial intelligence image processing technology in stomatology [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 40-46.
[3]	Cuijun Li, Gengbin Cai, Xiaoyin Liang, Yongyi Wang, Xinyi Zhan, Peiming Gu. Application of artificial intelligence in dental nursing [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 47-50.
[4]	Zetao Chen, Longshiyu Qiu, Zhuohong Gong, Hengyi Liu, Peisheng Zeng, Mengru Shi. Challenges and strategies for artificial intelligence-based quantitative measurement in oral implant [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 1-8.
[5]	Tinglin Yang, Tao Huang. Progress and challenges in the application of artificial intelligence for evaluating thyroid nodules [J]. Chinese Archives of General Surgery(Electronic Edition), 2026, 20(01): 60-65.
[6]	Haonan Mei, Rui Yang, Xiuheng Liu. Research progress of artificial intelligence-assisted pathological image analysis in the diagnosis of prostate cancer [J]. Chinese Journal of Endourology(Electronic Edition), 2026, 20(01): 1-7.
[7]	Xiaobo Ding, Jie Chen, Yanbo Wang. Progress in the application of artificial intelligence in the diagnosis and treatment of urinary calculi [J]. Chinese Journal of Endourology(Electronic Edition), 2026, 20(01): 15-21.
[8]	Fan Fan, Hao Huang, Lili Fu, Chunmei Zhou, Xuexia Ma, Hai Huang. Construction and effectiveness of intelligent urinary control standardized ward for patients with lower urinary tract dysfunction [J]. Chinese Journal of Endourology(Electronic Edition), 2026, 20(01): 44-50.
[9]	·Maimaiti Maiwulanjiang, ·Tulading Aliya, ·Tuxun Tuerhongjiang. Immunosuppression strategy for ABO-incompatible liver transplantation [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(02): 152-159.
[10]	Yufei Deng, Zhixin Wang, Ke Lou, Linxuan Zhang, Guichun Ma, Cuo Gang. Application of radiomics in precise diagnosis, efficacy evaluation, and treatment plan optimization of liver cancer [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(02): 172-180.
[11]	Chunyuan Yang, Xu Deng, Jingjing Wang, Cairang Yangdan, Wei Pan. Technical progress and clinical application of precise hepatectomy [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(02): 261-265.
[12]	Yue Tang, Jialu Chen, Delong Qin, Zonglong Li, Zhaohui Tang, Zhiwei Quan. Application and challenges of AI-based multimodal imaging in diagnosis and treatment of hepatocellular carcinoma [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(01): 4-9.
[13]	Yufang Liu, Qing Zhao. MRI in rectal cancer: from precision staging to prognostic prediction [J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2026, 15(01): 31-36.
[14]	Xiaokun Chen, Shunda Du. Advances and challenges of radiomics in hepatocellular carcinoma [J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2026, 16(02): 97-100.
[15]	Haonan Du, Guangjian Zhang, Jiawei Wang, Ting Liang, Ruiyang Suo, Jia Zhang. Application prospects of artificial intelligence in the diagnosis and treatment of pulmonary nodules [J]. Chinese Journal of Thoracic Surgery(Electronic Edition), 2026, 13(01): 64-73.

Please choose a citation manager

Content to export