Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL’s applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL’s limitations. Successfully addressing these hurdles is essential for enhancing FL’s efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem.

Mental health disorders contribute significantly to the global burden of disease, affecting quality of life and causing disability. These challenges are compounded by inequitable access to timely and effective mental health services, particularly in low-resource settings. Recently, artificial intelligence (AI) has emerged as a transformative tool in mental healthcare, offering novel approaches to enhance diagnosis, personalize treatment, and support continuous patient monitoring. This review explores the current landscape of non-generative AI applications in mental health, focusing on core methodologies such as machine learning, deep learning, and natural language processing. These techniques show promise in improving diagnostic accuracy, enabling adaptive and scalable digital therapy delivery systems, facilitating real-time mental health risk prediction through the analysis of multimodal data. According to our study, the majority of research demonstrated increased therapy personalization and diagnostic accuracy; however, significant challenges still exist due to low dataset diversity, algorithmic bias, and a lack of clinical validation. Ethical considerations and the need for transparent, explainable, and clinician-trustworthy AI are increasingly recognized as critical to successful implementation. Overall, AI-driven methods have strong potential to improve accessibility and effectiveness in mental health treatment, provided future studies prioritize equity, interpretability, and clinical relevance. We ran a narrative review between January 2019 to June 2025, screened in duplicate, and used thematic synthesis across diagnosis, therapy support, and monitoring.

Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients using various supervised, unsupervised, and deep learning approaches. Methods: This study presents HeartEnsembleNet, a novel hybrid ensemble learning model that integrates multiple machine learning (ML) classifiers for CVD risk assessment. The model is evaluated against six classical ML classifiers, including support vector machine (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), and random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) and ensemble techniques including stacking and voting. Results: Employing a dataset of 70,000 cardiac patients with 12 clinical attributes, our proposed model achieves a notable accuracy of 92.95% and a precision of 93.08%. Conclusions: These results highlight the effectiveness of hybrid ensemble learning in enhancing CVD risk prediction, offering a promising framework for clinical decision support.

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations.

DNA N6-methyladenine (6mA) has subsequently been identified as an important epigenetic modification which plays an important role in various cellular processes. The precise discrimination of N6-methyladenine (6mA) in genomes is required to recognize its biological functions. Although, we have several experimental techniques for the identification of 6mA-sites, in silico prediction has evolved as an alternative approach due to high-cost and labor-intense in experimental techniques. Taking into account, the implementation of an efficient and accurate model for identification of N6-methyladenine is of high priority. Several machine learning and deep learning models have already been developed to classify genome-wide 6mA sites. However, their success in predicting 6mA sites still has room for improvement. Based on this, we proposed a novel deep learning based model for the prediction of DNA N6-methyladenine sites in rice genomes. We built our model based on a special architecture called SpinalNet using DNA 6mA sites in rice genome and obtained an accuracies of 94.31% and 94.77% with an MCCs of 0.88 and 0.89 on two different datasets. The model generalizes well to other genomes as well, validated through cross-species testing. The results validate that the proposed model produces better scores than existing models regarding all evaluation parameters. A user-friendly webserver is made available at http://nsclbio.jbnu.ac.kr/tools/SpineNet6mA/.