Search Results (4)

Accurately assessing urban green perception is crucial for sustainable urban development and human well-being, yet conventional approaches often depend on simplistic objective metrics and non-immersive, screen-based subjective surveys, undermining ecological validity. This study develops and validates a multidimensional assessment framework that integrates Large Vision Models (LVMs) and Mixed Reality (MR) to couple objective environmental features with immersive human perception. The framework comprises 30 objective and 6 subjective indicators; state-of-the-art LVMs including DINOv2 and Depth Anything were applied to accurately extract objective features from Street View Imagery (SVI); and the MR device, Meta Quest 3, was utilized for the immersive collection of high-quality subjective data. In an empirical study with 74 volunteers in Shenzhen, China, machine learning models trained on MR-based data achieved 20–50% higher R² for subjective perception than models trained on traditional screen-based data. The validated framework was then applied to 61,131 SVIs citywide to map the spatial distribution of multidimensional green perception and to quantify relationships between objective and subjective indicators. Going beyond technical validation, this study demonstrates how the framework serves as a critical tool for urban planning and landscape upgrading. By diagnosing perceptual deficits where greening quantity does not translate into quality experiences, the framework supports a paradigm shift from quantity-oriented greening to perception-oriented spatial optimization. These findings offer actionable insights for policymakers to prioritize interventions that effectively enhance public health and environmental equity in high-density cities. Full article

Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical models. This review provides a holistic foundation by detailing the neurophysiological basis, recording techniques, and applications of EEG before providing a rigorous examination of traditional and modern analytical pillars. Statistical and Time-Series Analysis, Spectral and Time-Frequency Analysis, Spatial Analysis and Source Modelling, Connectivity and Network Analysis, and Nonlinear and Chaotic Analysis are explored. Afterwards, while acknowledging the historical role of Machine Learning (ML) and Deep Learning (DL) architectures, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), this review shifts the primary focus toward current state-of-the-art Artificial Intelligence (AI) trends. We place emphasis on the emergence of Foundation Models, including Large Language Models (LLMs) and Large Vision Models (LVMs), adapted for high-dimensional neural sequences. Finally, we explore the integration of Generative AI for data augmentation and review Explainable AI (XAI) frameworks designed to bridge the gap between “black-box” decoding and clinical interpretability. We conclude that the next generation of EEG analysis will likely converge into Neuro-Symbolic architectures, synergising the massive generative power of foundation models with the rigorous, rule-based interpretability of classical signal theory. Full article

Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in kidney care. GenAI supports research and early interventions through the generation of synthetic medical data. LLMs enhance clinical decision-making by analyzing medical texts and electronic health records, while LVMs improve diagnostic accuracy through advanced medical image analysis. Together, these technologies show promise for advancing patient education, risk stratification, disease diagnosis, and personalized treatment strategies. This paper highlights key advancements in GenAI, LLMs, and LVMs from 2018 to 2024, focusing on their applications in kidney care and presenting common use cases. It also discusses their limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, and ethical concerns. By providing a comprehensive analysis, this paper outlines a roadmap for integrating these AI advancements into nephrology, emphasizing the need for further research and real-world validation to fully realize their transformative potential. Full article

Large language models (LLMs) and large vision models (LVMs) have driven significant advancements in natural language processing (NLP) and computer vision (CV), establishing a foundation for multimodal large language models (MLLMs) to integrate diverse data types in real-world applications. This survey explores the evolution of MLLMs in radiology, focusing on radiology report generation (RRG) and radiology visual question answering (RVQA), where MLLMs leverage the combined capabilities of LLMs and LVMs to improve clinical efficiency. We begin by tracing the history of radiology and the development of MLLMs, followed by an overview of MLLM applications in RRG and RVQA, detailing core datasets, evaluation metrics, and leading MLLMs that demonstrate their potential in generating radiology reports and answering image-based questions. We then discuss the challenges MLLMs face in radiology, including dataset scarcity, data privacy and security, and issues within MLLMs such as bias, toxicity, hallucinations, catastrophic forgetting, and limitations in traditional evaluation metrics. Finally, this paper proposes future research directions to address these challenges, aiming to help AI researchers and radiologists overcome these obstacles and advance the study of MLLMs in radiology. Full article