AI, Volume 6, Issue 10 (October 2025) – 4 articles

The early detection of diabetes risk and effective management of patient data are critical for avoiding serious consequences and improving treatment success. This research describes a two-part architecture that combines an energy-efficient wireless communication technology with an interpretable deep learning model for diabetes categorization. In Phase 1, a unique wireless communication model is created to assure the accurate transfer of real-time patient data from wearable devices to medical centers. Using Lagrange optimization, the model identifies the best transmission distance and power needs, lowering energy usage while preserving communication dependability. This contribution is especially essential since effective data transport is a necessary condition for continuous monitoring in large-scale healthcare systems. In Phase 2, the transmitted multimodal clinical, genetic, and lifestyle data are evaluated using a one-dimensional Convolutional Neural Network (1D-CNN) with Bayesian hyperparameter tuning. The model beat traditional deep learning architectures like LSTM and GRU. To improve interpretability and clinical acceptance, SHAP and LIME were used to find global and patient-specific predictors. This approach tackles technological and medicinal difficulties by integrating energy-efficient wireless communication with interpretable predictive modeling. The system ensures dependable data transfer, strong predictive performance, and transparent decision support, boosting trust in AI-assisted healthcare and enabling individualized diabetes control. Full article

The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can identify and locate cracks without interrupting energy production, enabling timely maintenance. This study provides a comparative analysis and approach to the application and effectiveness of different vibration-based machine learning algorithms to detect the presence of cracks, identify the cracked blade, and locate the zone where the crack occurs in rotating blades of a small wind turbine. The datasets comprise root vibration signals, derived from healthy and cracked blades of a wind turbine in operational conditions. In this study, the blades are not considered identical. The sampling set dimension and the number of features were variables considered during the development and assessment of different models based on decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron algorithms (MLP). Overall, the KNN models are the clear winners in terms of training efficiency, even as the sample size increases. DT is the most efficient algorithm in terms of test speed, followed by SVM, MLP, and KNN. Full article

Background: This study presents a real-time mobile system for identifying mixed and partly covered foreign coins and banknotes using the You Only Look Once version 11 (YOLOv11) deep learning framework. The proposed system addresses practical challenges faced by travelers and visually impaired individuals when handling multiple currencies. Methods: The system introduces three novel aspects: (i) simultaneous recognition of both coins and banknotes from multiple currencies within a single image, even when items are overlapping or occluded; (ii) a hybrid inference strategy that integrates an embedded TensorFlow Lite (TFLite) model for on-device detection with an optional server-assisted mode for higher accuracy; and (iii) an integrated currency conversion module that provides real-time value translation based on current exchange rates. A purpose-build dataset containing 46 denominations classes across four major currencies: US Dollar (USD), Euro (EUR), Chinese Yuan (CNY), and Korean Won (KRW), was used for training, including challenging cases of overlap, folding, and partial coverage. Results: Experimental evaluation demonstrated robust performance under diverse real-world conditions. The system achieved high detection accuracy and low latency, confirming its suitability for practical deployment on consumer-grade smartphones. Conclusions: These findings confirm that the proposed approach achieves an effective balance between portability, robustness, and detection accuracy, making it a viable solution for real-time mixed currency recognition in everyday scenarios. Full article

Electronic Health Records (EHRs) have enhanced access to medical information but have also introduced challenges for healthcare providers, such as increased documentation workload and reduced face-to-face interaction with patients. To mitigate these issues, we propose RAGMed, a Retrieval-Augmented Generation (RAG)-based AI assistant designed to deliver automated and clinically grounded responses to frequently asked patient questions. This system combines a vector database for semantic retrieval with the generative capabilities of a large language model to provide accurate, reliable answers without requiring direct physician involvement. In addition to patient-facing support, the assistant facilitates appointment scheduling and assists clinicians by summarizing clinical notes, thereby streamlining healthcare workflows. Additionally, to evaluate the influence of retrieval quality on overall system performance, we compare two embedding models, gte-large and all-MiniLM-L6-v2, using real-world medical queries. The models are assessed within the RAG-Triad Framework, focusing on context relevance, answer relevance, and factual groundedness. The results indicate that gte-large, owing to its higher-dimensional embeddings, retrieves more informative context, resulting in more accurate and trustworthy responses. These findings underscore the importance of not only the potential of incorporating RAG-based systems to alleviate physician workload and enhance the efficiency and accessibility of healthcare delivery but also the dimensionality of models used to generate embeddings, as this directly influences the relevance, accuracy, and contextual understanding of retrieved information. This prototype is intended for the retrieval-augmented answering of medical FAQs and general informational queries, and is not designed for diagnostic use or treatment recommendations without professional validation. Full article