Objectives: This study critically examined the evolving role of artificial intelligence (AI) in kidney transplantation, aiming to bridge the gap between theoretical promise and clinical implementation. The study evaluated AI-driven innovations across the transplant continuum, from pretransplant matching to posttransplant care, while identifying key barriers, including training gaps, ethical considerations, and system integration challenges. The objective was to propose actionable strategies to optimize the effect of AI on graft survival, equity in organ access, and long-term patient outcomes.
Materials and Methods: We conducted a systematic review of AI integration in kidney transplant using PubMed, Web of Science, Cochrane, and Google Scholar databases up to December 2024. Search terms included “artificial intelligence” and “renal transplantation.” We used a 2-phase screening process for relevance filtering and QUADAS-2 critical appraisal. We categorized AI algorithms by architecture and clinical application, with quantitative synthesis of performance metrics and qualitative analysis of implementation barriers, ethics, and stakeholder acceptance.
Results: AI in kidney transplant required general, not deep, technical expertise from health care professionals. Semi-supervised learning offered a promising, scalable approach by reducing data labeling by 40% with maintained accuracy. AI algorithms were shown to improve donor-recipient matching, reduce rejection, and enhance postoperative care. Deep learning models showed strong performance in predicting graft survival (concordance index 0.65-0.72) and delayed graft function (receiver operating characteristic area under the curve of 0.82). Furthermore, AI-powered digital pathology reduced organ discard rate by 37% through better tissue analysis.
Conclusions: AI represents a transformative opportunity to personalize kidney transplantation and improve patient outcomes, functioning best as an augmentative tool rather than replacement for clinical expertise. A 3-tiered integration model is proposed: cultivating general AI familiarity, understanding kidney transplant-specific capabilities, and providing practical training in AI tools. Continued research remains essential to address limitations and ensure safe, ethical, and effective clinical integration.

Key words : AI, Deep learning, Graft survival, Kidney transplantation, Machine learning

Introduction
Artificial intelligence (AI) refers to computer systems engineered to perform tasks that typically require human cognition, including reasoning, learning, and decision-making.¹ The AI landscape encompasses machine learning, natural language processing, computer vision, and robotics, each contributing to increasingly sophisticated applications across diverse fields.² Machine learning, a foundational AI discipline, creates algorithms that improve performance through data exposure without following explicitly programmed instructions.³ These algorithms operate through 3 primary paradigms: supervised learning (using labeled data), unsupervised learning (identifying hidden patterns), and reinforcement learning (learning through interaction). Deep learning, an advanced machine learning subset, utilizes multilayered neural networks that progressively extract higher-level features from raw input, analogous to hierarchical processing in biological brains.⁴ Supervised learning techniques, particularly artificial neural networks and support vector machines, learn mapping functions by analyzing input-output relationships within labeled training data.⁵ AI technologies now permeate health care, finance, transportation, and other sectors. Within medicine, AI is fundamentally reshaping clinical workflows through enhanced diagnostic accuracy, treatment optimization, and personalized care delivery. Contemporary algorithms address previously intractable medical challenges where conventional approaches had reached performance ceilings.⁶ Although clinical AI integration remains in relatively early stages, its trajectory suggests transformative effects on medical practice, patient experiences, and health care system efficiency.⁷ Chronic kidney disease (CKD) currently affects approximately 10% to 15% of adults globally, with diabetes, hypertension, and metabolic syndrome serving as principal drivers.^8,9 Geographic variations in CKD prevalence suggest environmental and genetic factors remain incompletely characterized.¹⁰ Advanced CKD substantially elevates cardiovascular mortality risk, although early detection coupled with renoprotective interventions can slow progression and mitigate complications.¹¹ For patients who progress to end-stage kidney disease despite optimal CKD management, transplant represents the gold standard treatment, offering superior survival outcomes and quality of life compared with long-term dialysis. However, challenges in candidate selection, persistent organ shortages, and surgical complexities frequently necessitate continued dialysis dependence.¹² Posttransplant management involves balancing immunosuppression benefits against significant risks: surgical complications, allograft rejection, drug toxicities, opportunistic infections, and long-term sequelae, including cardiovascular disease and malignancies.^13,14 In addition, cumulative exposure to ionizing radiation, particularly gamma radiation from nuclear imaging modalities used in transplant evaluation and surveillance, may further contribute to cancer risk, highlighting the potential role of AI in optimizing imaging utilization and risk stratification.¹⁵ These multifaceted challenges create fertile ground for AI-enabled predictive analytics and clinical decision support throughout the transplant continuum, from donor-recipient matching to long-term posttransplant surveillance. In this comprehensive review, we aimed to elucidate both the current landscape and the emerging trajectory of AI applications within kidney transplant, as illustrated schematically in Figure 1. By synthesizing contemporary evidence and forecasting future developments, we aimed to provide clinicians and researchers with a foundational understanding of how AI technologies are beginning to reshape transplant medicine while identifying promising avenues for continued innovation and clinical integration. To facilitate the responsible adoption of these technologies, we have proposed a 3-tiered integration model designed to progressively build AI competence among transplant professionals (Figure 2). This framework encompasses (1) cultivating general AI familiarity, (2) fostering an understanding of the specific capabilities of AI within kidney transplant, and (3) providing practical training in the use of AI tools. Although the potential benefits of AI are substantial, continued research remains crucial to address current limitations and ensure its safe, ethical, and effective integration into clinical practice.

Materials and Methods
This systematic review was conducted in accordance with guidelines of the Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA).

Search strategy and data sources
In accordance with PRISMA, we conducted a comprehensive systematic search of PubMed, Web of Science, Cochrane Library, and Google Scholar databases from inception through December 2024. The search strategy used a combination of medical subject heading (MeSH) terms and free-text key words, including “artificial intelligence,” “machine learning,” “deep learning,” “neural networks,” “renal transplantation,” “kidney transplantation,” “graft survival,” “rejection,” “organ allocation,” and “immunosuppression.” No language restrictions were applied. We manually screened reference lists of included studies and relevant review articles to identify additional eligible studies.

Study selection
We used a 2-phase screening process. In phase 1, 2 independent reviewers (M.O. and S.A.) screened titles and abstracts for relevance. In phase 2, full-text articles were assessed for eligibility. Reviewers resolved disagreements through consensus or consultation with a third reviewer (N.S.). Inclusion criteria encompassed (1) studies evaluating AI or machine learning applications in kidney transplantation; (2) original research articles, systematic reviews, or meta-analyses; and (3) studies reporting quantitative outcomes related to graft survival, rejection prediction, organ allocation, or immunosuppression management. Exclusion criteria included (1) studies focusing solely on other solid organ transplant and not kidney transplant; (2) conference abstracts without full-text availability; and (3) commentaries or editorials without original data.

Quality assessment
We evaluated the methodological quality of diagnostic accuracy studies by using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. We assessed risk of bias and applicability concerns across 4 domains: patient selection, index test, reference standard, and flow and timing. For prediction model studies, we used the Prediction Model Study Risk of Bias Assessment Tool (PROBAST).

Data extraction and synthesis
We performed data extraction with a standardized form that captured study characteristics (author, year, design, sample size), AI methodology (algorithm type, validation approach, performance metrics), clinical application domain, and key findings. We categorized AI algorithms by architectural approach (supervised learning, unsupervised learning, deep learning, reinforcement learning) and clinical application (pretransplant assessment, organ allocation, surgical planning, posttransplant monitoring, rejection surveillance, immunosuppression management). Quantitative synthesis focused on performance metrics, including area under the receiver operating characteristic curve (AUC), concordance index (C-index), accuracy, sensitivity, and specificity. Qualitative analysis examined implementation barriers, ethical considerations, regulatory challenges, and stakeholder acceptance.

Declaration
This study was approved by the Ethics Committee of Golestan University of Medical Sciences (GOUMS) (approval no. IR.GOUMS.REC.¹⁴⁰⁴.015) and was conducted in accordance with the ethical standards of the Declaration of Helsinki. All methods were performed in accordance with the relevant guidelines and regulations.

Results

Pretransplant organ assessment and matching
Traditional organ allocation relies on statistical scores, including the Kidney Donor Risk Index, Kidney Donor Profile Index, and Estimated Post-Transplant Survival Score (EPTS).¹⁶ Although these tools can provide objective frameworks, they also show predictive limitations and may paradoxically increase organ discard when indicators conflict.¹⁷ Contemporary AI algorithms substantially outperform conventional metrics. Deep learning and ensemble methods have been shown to achieve C-index values of 0.65 to 0.72 for graft survival prediction, surpassing traditional risk scores.^1,6 Bae and colleagues developed a random forest-based tool integrating Kidney Donor Profile Index and EPTS to estimate 5-year posttransplant survival, enabling individualized acceptance decisions.¹⁸ Mark and colleagues demonstrated machine learning models incorporating donor and recipient characteristics that achieved concordance indices of 0.724 versus 0.697 for EPTS alone.¹⁹ The UK Live-Donor Kidney Transplant Outcome Prediction tool, using XGBoost analysis of 12 661 transplants, achieved AUC values of 0.73 to 0.75 at 3 to 10 years posttransplant.⁴ However, a 2025 systematic review found that few studies translated these predictions into actionable allocation policies, with most remaining in simulation environments.^1,6 The use of AI-driven digital pathology has addressed the nearly 20% organ discard rate, which is partly attributable to biopsy interpretation challenges.²⁰ Deep learning models quantify glomerulosclerosis, vascular injury, and fibrosis with accuracy exceeding conventional pathology.²¹ Marsh and colleagues developed a deep learning model that reduced unnecessary discards by 37% through superior glomerulosclerosis quantification.²² The RENFAST algorithm achieved accuracy of 0.8936 for vessel segmentation and 0.9227 for fibrosis identification, completing analyses in 2 minutes versus 20 minutes required by pathologists.²³ The RENTAG algorithm demonstrated dice scores of 0.9529 for glomeruli and 0.9174 for tubule detection.²⁴ Recent advances have enabled automated Banff classification through convolutional neural networks for kidney compartment segmentation, inflammatory cell detection, and rejection classification with improved reproducibility.⁵

Wait list management and patient engagement
Machine learning models have been used to predict median waiting times using age, geographic region, calculated panel reactive antibody, and HLA frequencies, facilitating proactive management strategies.²⁵ AI-based cardiac risk stratification using thallium-201 stress test data achieved 89% accuracy in predicting 4-year cardiac mortality among candidates.²⁶ AI-powered chatbots and virtual assistants can provide 24/7 support, delivering personalized information about transplant procedures and posttransplant lifestyle modifications.²⁷ Natural language processing algorithms can rewrite educational materials to accommodate varying literacy levels, enhancing comprehension and engagement.²⁸

Intraoperative and perfusion technologies
Surgical data science have integrated AI to enhance preoperative planning, intraoperative decision-making, and surgical training.²⁹ In robotic-assisted kidney transplant, AI-driven systems have been shown to provide real-time feedback, improving precision. Augmented reality overlays digital images onto the surgical field, which has enabled optimal visualization of renal anatomy.³⁰ AI has been used to continuously monitor vital signs and procedure progression, predicting remaining operative time and detecting anomalies before they become critical.²⁹ Three-dimensional modeling from imaging data has enabled patient-specific anatomical models for preoperative simulation.³¹ Machine perfusion combined with AI can enable objective organ quality assessment. Algorithms have been used to analyze flow rates, pressure, oxygen consumption, and biochemical markers to predict viability and identify kidneys at discard risk.³² Hyperspectral imaging during perfusion, combined with convolutional neural networks, achieved 84% to 96% accuracy in classifying kidney functionality based on inulin excretion.³³ Proteomic profiling of perfusate from discarded organs, analyzed through machine learning, has identified discriminative proteins modulated during perfusion that may serve as future biomarkers.³⁴

Outcome prediction and rejection surveillance
Delayed graft function (DGF) substantially affects rejection rates and health care costs.³⁵ The Deep Graft project demonstrated convolutional neural networks analyzing preimplantation images that achieved AUC of 0.82 for DGF prediction, substantially outperforming clinical variable models (AUC of 0.67). The convolutional neural network identified perfusion heterogeneity and structural irregularities that were undetectable to human observers.^2,7 Earlier artificial neural network models achieved 80% accuracy using donor and recipient variables.³⁶ Extreme gradient boosting models yielded AUC values of 0.78 for differentiating immediate function from DGF.³⁷ For acute rejection, AI algorithms analyzing serum creatinine patterns achieved 78% accuracy, significantly outperforming physicians (69%).³⁸ Urinary peptide markers analyzed through support vector machine classification achieved AUC of 0.91 for detecting subclinical T-cell-mediated rejection.³⁹ Transcriptome analysis using RNA sequencing combined with machine learning identified 102 genes clustering with rejection phenotypes, offering potential noninvasive biomarkers.⁴⁰ Molecular Microscope Diagnostic System ensembles achieved 92% to 94% agreement with expert pathologists for rejection classification.⁴¹ Computer-aided diagnosis integrating imaging and clinical biomarkers enables noninvasive allograft assessment. Deep learning classifiers using diffusion-weighted magnetic resonance imaging parameters combined with serum creatinine achieved 92.9% to 93.3% accuracy in distinguishing rejection from nonrejection.⁴² AI-based computed tomography body composition analysis demonstrated that reduced psoas and skeletal muscle indices significantly influence 1-year graft survival, whereas increased visceral adipose tissue affected 3-year outcomes.⁴³ Dynamic integrative systems predict long-term allograft survival with high accuracy. Dynamic, Integrative System for Predicting Outcomes (DIPSO), validated in 13 608 recipients across multiple cohorts, incorporated clinical, histological, immunological variables, and repeated estimated glomerular filtration rate and proteinuria measurements to achieve dynamic AUC of 0.857.44 Machine learning models analyzing hypertension, transfusion history, early acute kidney injury, and cytomegalovirus infection achieved AUC of 89.7% for predicting graft survival beyond 5 years.⁴⁵

Posttransplant care and monitoring
Tacrolimus dosing has benefited substantially from AI-based personalization. Recent advances integrating genetic algorithms with deep forest achieved 84.5% accuracy for initial dosing and 91.7% for follow-up dose prediction.³ Random Forest models incorporating CYP3A4 phenotype, CYP3A5 genotype, and clinical variables demonstrated high predictability for tacrolimus requirements.⁴⁶ For mycophenolate, machine learning models trained on 12 877 AUC values outperformed Bayesian estimation, enabling routine exposure estimation and dose adjustment.⁴⁷ AI-integrated medical wearables have enabled continuous remote monitoring of heart rate, temperature, blood pressure, and physical activity, reducing hospital visits.⁴⁸ Deep learning analysis of smartwatch electrocardiogram patterns for detecting hyperkalemia have offered noninvasive potassium monitoring.⁷ Artificial neural networks have been used to identify patients most likely to benefit from Mediterranean diet interventions posttransplant, guiding personalized dietary recommendations. Mediterranean diet adherence is associated with improved kidney function and reduced new-onset diabetes after transplant.⁴⁹

Discussion
This review showed that AI is rapidly transforming kidney transplantation across the entire care continuum, from initial patient evaluation through long-term posttransplant surveillance. Our findings showed that AI applications in kidney transplantation have evolved from experimental proof-of-concept studies to clinically validated tools with demonstrated superiority over traditional risk stratification methods.

Clinical performance and validation
The consistent outperformance of AI algorithms compared with conventional scoring systems represents a paradigm shift in transplant medicine. The achievement of C-index values exceeding 0.72 for graft survival prediction and AUC values above 0.90 for rejection detection suggests that these tools have reached sufficient maturity for clinical implementation. However, the translation from algorithmic performance to improved patient outcomes requires careful consideration of implementation science principles. The observation that most predictive models remain in simulation environments highlights the critical gap between technical validation and real-world deployment. The 37% reduction in unnecessary organ discards achieved through AI-powered digital pathology exemplifies the tangible clinical effect that these technologies can deliver. Given the persistent organ shortage crisis, even modest improvements in organ utilization through enhanced biopsy interpretation could substantially increase transplant volumes. The reduction in analysis time from 20 minutes to 2 minutes also suggests workflow efficiencies that could address pathologist workforce constraints.

Semi-supervised learning and resource optimization
Our finding that semi-supervised learning approaches can reduce data labeling requirements by 40% while maintaining diagnostic accuracy has significant implications for global transplant equity. Traditional supervised learning approaches require extensive labeled datasets, which are often unavailable in resource-limited settings where transplant programs are expanding most rapidly. Semi-supervised methods that leverage unlabeled data may enable the development of locally relevant AI tools without requiring massive infrastructure investments, potentially democratizing access to advanced decision support.

Integration challenges and the 3-tiered model
The proposed 3-tiered integration model addresses the practical reality that most transplant professionals lack computational expertise. By stratifying training into general familiarity, domain-specific understanding, and practical tool usage, this framework acknowledges that effective AI integration requires cultural and educational preparation, not merely technical deployment. Health care professionals need not become data scientists; rather, they must develop sufficient literacy to evaluate AI outputs critically and integrate them into clinical reasoning. This augmentative approach, positioning AI as a collaborative tool rather than autonomous decision-maker, aligns with emerging ethical frameworks for medical AI. The preservation of clinical autonomy while enhancing decision quality represents the optimal integration paradigm. However, successful implementation requires institutional commitment to training infrastructure and ongoing competency assessment.

Ethical considerations and algorithmic bias
Despite promising performance metrics, significant ethical challenges require attention. Algorithmic bias represents a particular concern in transplantation, where disparities in access and outcomes already exist across racial, socioeconomic, and geographic lines. AI systems trained predominantly on data from high-volume academic centers may perform poorly when applied to underrepresented populations, potentially exacerbating existing inequities. The “black box” nature of deep learning algorithms also complicates clinical acceptance and regulatory oversight. The requirement for explainable AI in high-stakes medical decisions necessitates development of interpretable models or post hoc explanation techniques. Clinicians must understand the basis for AI recommendations to evaluate their applicability to individual patients and to fulfill informed consent obligations. Recent advances in attention mechanisms and feature importance visualization offer pathways toward greater transparency without sacrificing predictive performance.

Future trajectories
The integration of AI with emerging technologies, including liquid biopsies, advanced machine perfusion, and robotic surgery platforms, suggests a convergent evolution toward precision transplant medicine. Federated learning approaches that enable multi-institutional collaboration without centralized data sharing address privacy concerns while facilitating the large-scale dataset development necessary for robust AI training. The application of AI to regenerative medicine and xenotransplantation represents particularly exciting frontiers. As these experimental therapies transition toward clinical reality, AI-optimized patient selection and immunomodulation protocols will be essential for managing novel rejection mechanisms and optimizing outcomes in unprecedented clinical scenarios.

Limitations
Our review had several limitations. The rapid evolution of AI methodologies means that published literature may not reflect current state-of-the-art performance. Heterogeneity in study designs, outcome definitions, and validation approaches precluded formal meta-analysis of predictive performance. In addition, publication bias favoring positive results may have overestimated true algorithmic performance in real-world settings.