Application of Artificial Intelligence in Kidney Transplantation

Application of AI in preoperative evaluation and donor–recipient matching for kidney transplantation The shortage of donor kidneys remains a major challenge in transplantation worldwide. Efficient and appropriate donor–recipient matching is essential to maximize the use of limited kidney resources. AI algorithms can rapidly process massive datasets—including blood type, human leukocyte antigen compatibility, age, medical history, and genetic information of donors and recipients—while identifying complex correlations to build accurate matching models. With precise AI-assisted matching, not only can transplantation success rates be improved, but the likelihood of postoperative rejection can also be reduced. For example, organ allocation systems that incorporate machine learning can integrate multiple variables, quantitatively evaluate donor–recipient compatibility, assist clinicians in identifying the most suitable kidney, and ultimately improve patient and graft prognoses [2]. In addition, AI can provide comprehensive assessments of marginal donor kidneys to determine their transplant suitability, thereby expanding the donor pool [3]. Application of AI in surgical planning and assistance for kidney transplantation In the surgical planning stage, AI can leverage medical imaging data, such as that obtained from computed tomography and magnetic resonance imaging, together with three-dimensional reconstruction and image recognition technologies to generate detailed three-dimensional models of patient tissues and blood vessels. These visualizations help surgeons anticipate anatomical characteristics and variations, develop personalized surgical plans, and reduce operative risks. During surgery, intelligent assistance systems can provide real-time data support and guidance. For example, augmented reality (AR) technology can project virtual surgical planning information directly onto the operative field, helping surgeons perform critical steps such as vascular anastomosis and kidney implantation with greater accuracy. This approach may enhance surgical precision and stability, shorten the operative time, and reduce intraoperative bleeding and tissue injury [4]. AI optimizes postoperative management and complication prediction in kidney transplantation Following kidney transplantation, patients require lifelong immunosuppressive therapy and close monitoring of multiple clinical indicators to prevent rejection and other complications. AI can enable comprehensive health surveillance by analyzing clinical data, laboratory results, genetic information, and even real-time physiological signals from wearable devices. In the area of immunosuppressive drug management, AI can predict individual responses to different medications and determine optimal dosages based on factors such as age, weight, liver and kidney function, and pharmacogenomic profiles. This supports precise, personalized therapy and helps avoid both rejection and drug-related toxicity [5]. Moreover, by learning from large datasets of transplant recipients, AI-driven models can predict the risk of complications such as delayed graft function, acute rejection, infections, and cardiovascular disease, allowing for earlier intervention and improved outcomes [6]. AI-based automated analysis of transplant kidney pathology can also more accurately identify rejection subtypes and other pathological features, providing valuable support to clinicians—particularly in centers with limited access to specialized transplant pathologists [7]. Challenges and limitations of AI application in the field of kidney transplantation Despite its great potential, AI in kidney transplantation still faces significant challenges. Data quality is a primary concern because the accuracy of AI models depends on access to large volumes of reliable data. Yet medical datasets are often incomplete, inaccurate, or inconsistently formatted, which undermines both model training and prediction accuracy. Patient privacy presents another critical issue. Ensuring data security and protecting personal information during collection, storage, transmission, and use remain pressing concerns. The interpretability of AI models also poses difficulties. Many algorithms, particularly deep learning neural networks, function as "black boxes" with decision-making processes that are difficult to explain. This lack of transparency may erode trust among clinicians and patients. Given that medical decisions directly affect patients' lives and health, physicians must understand the rationale behind AI-generated recommendations before they can confidently integrate them into practice. Finally, the application of AI raises legal, regulatory, and ethical questions. Certain issues, such as defining responsibility when AI-assisted decisions contribute to medical errors and determining whether AI-generated medical advice carries legal authority, require further clarification and oversight. In summary, AI offers tremendous opportunities for kidney transplantation, with clear value across preoperative evaluation, surgical planning, and postoperative management. It holds promise for improving long-term graft survival and enhancing patients' quality of life. However, widespread adoption requires overcoming challenges related to data quality, model interpretability, privacy protection, and regulatory frameworks. Looking ahead, as technology advances and supporting systems mature, AI is poised to play an even greater role in kidney transplantation, bringing new hope to patients with end-stage renal disease. Guodong Chen: writing – original draft, writing – review and editing. The author has nothing to report. The author has nothing to report. The author has nothing to report. Professor Guodong Chen is Executive Editor-in-Chief of the Organ Medicine. To minimize bias, they were excluded from all editorial decision-making related to the acceptance of this article for publication. Data sharing is not applicable to this article, as no new data were created or analyzed in this study.