Diabetic complications are a major cause of disability and mortality among patients, and early identification of high-risk individuals is essential for precision prevention and management. In recent years, the rapid advancement of artificial intelligence (AI) has provided transformative tools for risk prediction and clinical decision support in diabetes care. In this narrative review, we systematically surveyed studies published between January 2015 and June 2025 in PubMed, Web of Science, and Scopus that applied AI-based predictive modeling for three major diabetic complications: diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic cardiovascular disease (CVD). A total of 58 studies were included, encompassing models based on clinical features, molecular omics, medical imaging, and multimodal data integration. Cross-scale and multimodal data fusion has emerged as a promising new paradigm, demonstrating improved predictive performance over single-modality approaches in three major diabetic complications. We also summarize the evolution from traditional machine learning to deep learning and, more recently, to large language models and agent-based systems, comparing their methodological characteristics, strengths, and suitable application scenarios. Finally, we proposed an actionable six-step framework and clinical translation pathway for AI in diabetic complications, outlining key steps from data curation and model development to validation, regulatory compliance, and real-world implementation. Together, these insights provide a roadmap toward developing robust, transparent, and clinically deployable AI systems capable of transforming the prevention and management of diabetic complications.

Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.

Background: The expression of secretory proteins is involved in each stage of biomass from fetal development to the immune response. As an animal model for the study of human diseases, the study of protein secretion in pigs has strong application prospects. Objective: Although secretory proteins play an important role in cell activities, there are no machine learning-based approaches for the prediction of pig secretory proteins. This study aims to establish a prediction model for identifying the secretory protein in Sus scrofa. Methods: Based on the pseudo composition of k-spaced amino acid pairs feature encoding method and support vector machine algorithm, a prediction model was established for the identification of the secretory protein in Sus scrofa. Results: The model produced the AUROC of 0.885 and 0.728 on the training set and independent testing set, respectively. In addition, we discussed features used for the prediction. Conclusion: In this study, we proposed the first classification model to identify secretory proteins in Sus scrofa. By learning the characteristic of secretory proteins, it may become feasible to design and produce secretory proteins with distinctive properties that are currently unavailable.

Background: Hypertension remains a major public health concern worldwide, particularly among middle‐aged and elderly populations; in China, the prevalence among adults has reached 31.6%, and the burden in rapidly urbanizing regions such as Chengdu continues to rise with changing dietary and lifestyle patterns. Identifying individuals with hypertension in hospital‐based check‐up settings is important for timely management. Machine learning, which can capture nonlinear relationships among routinely collected questionnaire‐based variables, may improve the classification of prevalent hypertension in this population. Objective: This study aimed to assess the prevalence of hypertension among middle‐aged and elderly residents in Chengdu, China, to identify key factors associated with hypertension, and to compare the discrimination and calibration of multiple machine learning algorithms in classifying prevalent hypertension. Methods: A total of 14,573 individuals aged ≥ 45 years who underwent routine physical examinations at a tertiary general hospital in Chengdu between January and December 2023 were included. Demographic and lifestyle data were collected using standardized questionnaires. Participants were randomly divided into a training set ( n = 13,116) and a validation set ( n = 1457) in a 9:1 ratio using stratified sampling. To avoid feature‐selection leakage, univariate analysis, multivariable logistic regression (LR), and collinearity diagnosis were performed exclusively within the training set, yielding 11 independent predictors; the validation set did not participate in any feature‐ or model‐selection step. Four machine learning models, extreme gradient boosting (XGBoost), random forest (RF), LR, and least absolute shrinkage and selection operator (LASSO), were developed, with the synthetic minority oversampling technique applied only to the training folds and hyperparameters tuned by grid search with 10‐fold stratified cross‐validation. A nomogram was constructed based on the LR model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, Youden index, and Brier score, with the classification cutoff determined by maximizing the Youden index in the training set. Results: Among the 14,573 participants, 2823 (19.4%) were diagnosed with hypertension. Eleven independent predictors were identified, including age, sex, employment status, family history of hypertension, satiation level, dietary preferences (high‐salt, spicy, and high‐fat diets), combined diabetes, other cardiovascular disease, and subjective mental stress. In the validation set, all four models showed good discrimination, with AUCs of 0.936 (XGBoost), 0.935 (RF), 0.920 (LR), and 0.920 (LASSO) and accuracy above 0.85; LASSO achieved the highest sensitivity (0.904). Calibration was good for all models (bootstrap Brier scores < 0.15), and decision curve analysis indicated positive net clinical benefit across threshold probabilities of 5%–95%. Conclusion: Hypertension was common among middle‐aged and elderly adults attending health examinations in Chengdu, China, affecting 19.4% of the participants. All four machine learning models classified prevalent hypertension accurately. XGBoost and RF achieved the highest discrimination, while LR and LASSO were better calibrated. These models may help identify hypertensive individuals in hospital‐based check‐up settings, although their performance still needs to be confirmed in external populations.