## 2 State of the Art and Related Works
The integration of AI with urinalysis for disease diagnosis and treatment is also discussed [43], emphasizing its revolutionary impact on healthcare. Additionally, predictive models for AKI using ML highlight the importance of considering baseline serum creatinine (sCr) levels, as performance varies with different estimation methods [44]. Another study [45] integrates deep learning with 1D-CNNs and LSTM for diagnosing Pancreatic Ductal Adenocarcinoma (PDAC), achieving high accuracy (97%) and AUC (98%) using urine proteomic biomarkers. Similarly, ML models, particularly XGBoost, have been applied to predict end-stage renal disease (ESRD) risk in type 2 diabetes patients using clinical data [46]. AI's capacity to predict postoperative acute kidney injury (AKI) after cardiothoracic surgery using recurrent neural networks (RNNs), a subset of ANNs, is also explored in [47], demonstrating superior prediction accuracy (AUC of 0.893) compared to clinicians. The integration of such AI models into electronic health records (EHRs) can facilitate real-time patient monitoring and early intervention. The study [48] further underscores the importance of accurate diagnostic information in improving patient outcomes and reducing healthcare costs. An ML model predicting 5-year kidney transplant survival achieved an AUC of 89.7%, showcasing its potential for early detection of graft status [49]. The application of AI continues to expand with automated systems for diagnosing kidney stones from CT images, marking significant advancements in AI-driven medical imaging interpretation [50]. Moreover, studies [51; 52] explore smartphone-based systems for diagnosing microalbuminuria and quantifying albuminuria, demonstrating high accuracy across various conditions. The integration of AI with various healthcare domains is also explored in [53; 54]. These studies demonstrate how AI optimizes resource allocation and improves kidney disease diagnosis through advanced algorithms and models. Electrochemical energy mechanisms for early kidney failure detection are explored in [55], showcasing AI's role in streamlining data analysis and enhancing diagnostic accuracy for preemptive interventions. The literature also emphasizes AI's potential in supporting clinicians in diagnosing, prognosticating, and treating kidney diseases, and stresses the need for further advancements in AI to address the significant burdens posed by acute kidney injury and chronic kidney disease [56; 57]. Several studies [58; 59; 60] highlight the growing use of AI in diagnosing kidney diseases, especially AKI and CKD, employing various approaches such as machine learning ensembles, deep learning, and federated learning. It is worth mentioning that medicine, and especially nephrology, are not the only applications of AI and ML. Other applications of these engineering solutions are presented in [61; 62; 63; 64; 65]. A compact comparative overview of AI applications in nephrology is presented in tabular form (see Table 1). In sum, artificial intelligence is transforming nephrology by providing more accurate diagnostic tools, predictive models, and decision support systems. As AI continues to evolve, its integration into nephrology promises to improve patient care, enhance diagnostic capabilities, and streamline healthcare practices, marking a significant advancement in the field. These works collectively illustrate the growing influence of expert systems, fuzzy logic, and machine learning in nephrology, with notable applications in risk prediction, diagnosis, and treatment planning. However, they also highlight the need for rigorous validation, ethical considerations, and unbiased model development to ensure the effective and equitable application of AI and ML in nephrology.