## 2 State of the Art and Related Works
Moreover, Ref. [15] advocates cautious optimism for AI in acute kidney injury (AKI) care, highlighting the need for rigorous evaluation and unbiased model development to ensure clinical effectiveness. The integration of artificial intelligence and machine learning in nephrology diagnostics is further evidenced by studies such as [16], which address challenges in developing robust risk prediction models. These challenges include the need for high-quality data and comprehensive performance measurement strategies. Additionally, Ref. [17] highlights the potential biases in AI-driven clinical decisions, proposing strategies to mitigate these biases to ensure fair and equitable AI utilization. In chronic kidney disease (CKD) detection, studies such as [7,18] employ ML models to predict disease progression and enhance risk stratification. For instance, Ref. [19] demonstrates the application of ML in predicting tacrolimus blood concentration, underscoring its potential in personalized medication management. Furthermore, ML techniques such as support vector machines (SVMs) and ensemble methods have shown significant promise in early CKD detection. Studies like [20, 21, 22, 23] emphasize the role of these methods in improving diagnostic accuracy, enabling timely intervention and better patient outcomes. The integration of these models into clinical practice marks a transformative shift in nephrology care. Accurate measurements are pivotal to the success of predictive models in biomedicine. These measurements are vital for predicting and analyzing laboratory test results in nephrology [24, 25, 26], as well as for broader applications in biomedicine, including spatial modeling and EEG analysis [27, 28, 29]. Such data also support decision-making in kidney diseases [30, 31, 32]. Challenges in the development of predictive models extend beyond diagnostics. Issues such as modeling complexity [33], parameter estimation [34], and healthcare monitoringsupport systems [35] underscore the need for ongoing advancements in ML methodologies. Addressing these challenges is essential to ensure the successful integration of these technologies into clinical workflows and their reliable application in nephrology. The study [36] employs ML models to predict short-term prognosis for severe acute kidney injury (AKI) patients undergoing prolonged intermittent renal replacement therapy (PIRRT). By analyzing 493 hospitalized AKI patients, the study identifies key factors, such as electrolyte levels and comorbidities, which influence survival and renal recovery. Various ML algorithms, including Naive Bayes, random forest, and K-nearest neighbors, effectively predicted these outcomes, underscoring the importance of electrolyte management for improving prognosis. Additionally, the work [37] introduces a multiple linear regression model using [PERSON]'s fuzzy inference system, a type of rule-based AI, which outperforms traditional methods, demonstrating superior performance even with limited datasets. The review also highlights the use of health-disease phase diagrams (HD-PDs) for precision medicine, utilizing AI techniques to visualize disease onset probabilities based on biomarkers. HDPDs are identified as a powerful tool for identifying intervention targets and preventing disease onset in many cases [38]. Furthermore, CKD.Net, a hybrid model combining S-MTL, SimpleRNN, and MLP, demonstrates its ability to predict chronic kidney disease (CKD) stages with remarkable accuracy (99.2-99.8%) and represents a step forward in real-time, non-invasive diagnosis in clinical practice [39]. This highlights the importance of Artificial Neural Network (ANN) applications. AI also features prominently in medical imaging and diagnostic tools. For example, one study [40] uses ultrasonography to measure kidney volume in children, outperforming traditional methods. Other studies explore ML's role in detecting biomarkers for Papillary Renal Cell Carcinoma (PRCC) [41] and predicting complications in diabetic kidney disease [42].