DeepMind, a leading AI research division of Google, unveiled an AKI predictive model in 2019, utilizing a dataset comprising approximately 6 billion independent entries, including 620,000 features. The dataset was divided into training (80%), validation (5%), calibration (5%), and test (10%) sets. The model demonstrated the capability to predict 55.8% of AKI cases up to 48 h in advance. Notably, its predictive accuracy surged to over 90% for the most severe AKI cases [ future of AKI prediction using machine learning (ML) looks promising, with significant advancements anticipated. A crucial factor will be the expanding volume of data stored in electronic health records (EHRs), including genetic, metabolic, and laboratory data, and more comprehensive integration of patient medical histories, such as high-resolution data from previous hospital admissions. Another significant development is the advancement of natural language processing (NLP) and its application in medicine. Large language models (LLMs) have revolutionized infor­mation retrieval and text generation with remarkable accuracy. Some models are now capable of incorporating clinical notes processed by NLP into predictive models. For instance, a study focusing on AKI prediction in critically ill patients utilized clinical notes from the first 24 h of ICU admission, sourced from the Medical Information Mart for Intensive Care III (MIMIC-III) database. This approach achieved an impressive area under the curve (AUC) of 0.779 by generating concept representations of clinical notes through NLP, utilizing five supervised learning classifiers and knowledge-guided deep learning algorithms [ real-time waveform data analysis, including arterial blood pressure, EKG, central venous pressure, and respiratory data, opens new avenues for AKI prediction