Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Artificial Intelligence (AI) has emerged as a promising approach to enhance disease surveillance and support outbreak prediction in public health. Conventional surveillance systems, while foundational, are often limited by reporting delays, under-detection, and challenges in handling large and complex data streams. Recent advances in AI including machine learning, natural language processing, and deep learning offer new opportunities to address these limitations by enabling automated case detection, syndromic surveillance, real-time anomaly detection, and predictive modeling. This review synthesizes current evidence on AI-driven approaches to disease surveillance and outbreak prediction, focusing on methodological frameworks, data sources, and applications across infectious disease contexts. Key AI-based surveillance strategies, outbreak prediction models, and forecasting techniques are discussed alongside emerging data sources such as electronic health records, environmental data, mobility data, and digital media. The review also highlights challenges related to data quality, interpretability, ethical considerations, and integration with traditional surveillance systems. By summarizing existing knowledge, this review aims to inform future research and support the responsible adoption of AI technologies in public health surveillance and outbreak preparedness....
Multi-agent artificial intelligence (MAAI) represents a foundational shift in the automation of knowledgework,moving beyond static workflows toward adaptive systems of interacting AI-based agents. These agents perceive, reason, and coordinate in real time to address complex, context-rich tasks that traditionally require human expertise. Drawing on the conceptual roots of process automation, agentic information systems, and AI, this paper introduces a structured, five-component framework that conceptualizes MAAI as a layered architecture composed of foundation model, data-centric perception and action, dynamic orchestration, agent-integrated workflow, and interaction interface. This framework disentangles the technical, organizational, and human-facing dimensions of MAAI, offering researchers and practitioners a systematic lens to analyze and design agentbased AI automation. The framework further structures three research pathways focused on advancing technical capabilities, enabling organizational integration, and addressing socio-technical implications such as fairness, accountability, and labor transformation. Together, these contributions establish a foundation for interdisciplinary inquiry into how MAAI reshapes work, coordination, and digital value creation....
Accurate and efficient image segmentation is crucial in anatomy, histology, and pathology research. Conventional manual approaches are time- consuming, whereas fully automated artificial intelligence segmentation requires substantial manual correction owing to inaccuracy. To address this, we developed SegRef3D, a tool integrating the Segment Anything Model 2 with multiframe tracking and interactive refinement functions, enabling streamlined segmentation workflows for anatomical research. SegRef3D is implemented as a standalone, offline desktop application that operates entirely in a local environment, eliminating the need for cloud- based services. SegRef3D provides a unified workflow from data import to segmentation, object tracking, refinement, and three- dimensional model export. Users can specify segmentation prompts through bounding box input, track objects across multiple frames with start–end range selection, and refine results using intuitive Add to Mask and Erase from Mask tools. Up to 20 objects can be handled simultaneously, with each assigned a unique color. The software supports the Standard Tessellation Language output for three- dimensional modeling and includes volume measurement functions. The SegRef3D prototype, called Seg&Ref, has been applied in studies using serial histological sections, correlative microscopy with block- face imaging, and pelvic magnetic resonance imaging. Building on these applications, SegRef3D further enhances usability and enables a seamless workflow. SegRef3D offers an accessible, efficient, and accurate segmentation environment tailored for morphological and anatomical studies. Combining artificial intelligence- powered automatic segmentation with human- guided refinement in a user- friendly graphical user interface bridges the gap between research needs and computational methods. By supporting applications that span traditional anatomy and modern pathology, SegRef3D provides a versatile platform for integrative morphological analysis. Its open- source availability ensures its broad applicability in research, education, and clinical training in the anatomical sciences....
This paper explores how artificial intelligence (AI) can enhance operational efficiency and financial performance in the short-term rental market. Using a qualitative single case study of Solarento, a new technology-driven operator in Poland, the study examines the implementation of AI in revenue management, demand forecasting, operational automation, and owner-facing financial transparency. The research integrates comparative insights from two established competitors: Sun & Snow and Downtown Apartments, to better contextualize the AI-driven approach. Data sources include internal reports, public statistics, market studies, and the company’s digital performance dashboards. Thematic analysis identifies key patterns and differentiators across five strategic areas. Findings show that AI-enabled models not only reduce operational costs and vacancy periods, but also improve guest experience and owner satisfaction through greater transparency and adaptive pricing. Despite the operational and economic benefits, the study highlights several challenges such as data dependency, algorithmic opacity, and ethical risks related to automation and guest profiling. The paper concludes with a set of recommendations for implementing hybrid models that balance automation with human oversight. This research contributes to the growing literature on digital transformation in the hospitality industry and provides practical insights for rental operators aiming to remain competitive in a data-driven economy....
1. Ecology and artificial intelligence (AI) are becoming increasingly intertwined. Originally, the intersection between the two disciplines was driven by a critical need for AI to help process rapidly growing volumes of ecological data. Early applications primarily entailed applying AI methods to automate relatively basic tasks, such as detecting blank images from camera traps. However, researchers in both disciplines are beginning to recognize the potential for transformative advances when AI is fully integrated into ecological research and conservation practice. 2. This special feature presents research at the cutting edge of the AI–ecology interface, focusing on work that advances the state of both fields beyond proof-of- concept to true interdisciplinary insight. 3. The papers in this collection reveal a maturing field that balances technical advancement with ecological relevance. They address both methodological challenges and the critical need for meaningful integration between computer science innovations and fundamental ecological questions. 4. As a whole, this collection demonstrates the potential for AI to enhance both fundamental ecological understanding and applied conservation efforts, as well as to bridge the gap between scientific discovery and policy implementation. The special feature underscores the importance of genuine interdisciplinary collaboration in developing technologies that not only showcase technical prowess, but also address pressing ecological challenges and support evidence-based decision-making in biodiversity conservation....
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