The rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In smart healthcare, opportunities and challenges coexist. This article provides a comprehensive overview of past developments and recent progress in this area. First, we summarize the definition and characteristics of smart healthcare. Second, we explore the opportunities that AI technology brings to the smart healthcare field from a macro perspective. Third, we categorize specific AI applications in smart healthcare into ten domains and discuss their technological foundations individually. Finally, we identify ten key challenges these applications face and discuss the existing solutions for each.

The healthcare sector is undergoing a significant transformation due to the rapid expansion of data and advancements in digital technologies. The increasing complexity of healthcare data, including electronic health records (EHRs), medical imaging, and patient monitoring, underscores the necessity of big data technologies. These technologies are essential for enhancing decision-making, personalizing treatments, and optimizing operations. Digitalization further revolutionizes healthcare by improving accessibility and convenience through technologies such as EHRs, telemedicine, and wearable health devices. Cloud computing, with its scalable resources and cost efficiency, plays a crucial role in managing large-scale healthcare data and supporting remote treatment. However, integrating cloud computing in healthcare, especially in remote areas with limited network infrastructure, presents challenges. These include difficulties in accessing cloud services and concerns over data security. This article proposes a smart healthcare system utilizing the edge-cloud continuum to address these issues. The proposed system aims to enhance data accessibility and security while maintaining high prediction accuracy for disease management. The study includes foundational knowledge of relevant technologies, a detailed system architecture, experimental design, and discussions on conclusions and future research directions.

In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments.

Rapid and accurate detection of Salmonella serotypes associated with foodborne illness is critical for maintaining food safety. This study developed and evaluated the capability of a machine learning-enabled paper chromogenic array sensor (ML-PCA) approach for non-destructive, continuous, and simultaneous detection and differentiation of Salmonella enterica serotypes Salmonella Typhimurium (ST) and Salmonella Enteritidis (SE) in chicken breast in the presence of background microflora during storage at room temperature and refrigerated temperature (4°C). At room temperature, the ML-PCA approach achieved accuracies of 80–88% for detecting ST and SE in mono-serotype, dual-serotype, and serotype cocktail as early as 6 h. At 4°C, Salmonella serotypes were detected as early as 1 d, with testing accuracy exceeding 80%. These results demonstrated that the ML-PCA approach enables non-destructive, continuous, and simultaneous detection and differentiation of ST and SE on poultry meat. This methodology offers a potential tool for continuous Salmonella monitoring across the food system.