About this Research Topic
Healthcare is an essential part of city life. A healthcare system involves distinct parties (e.g., patients, primary care physicians, pharmacists, specialists, and other experts) and different stages (including health condition monitoring, disease diagnosis, medical treatment, and rehabilitation). Recent years have witnessed the rapid growth of population density in cities, the steadily aging population, and the rise in chronic illness, which poses grand challenges on existing healthcare systems, such as the high demand on hospitals, medical personnel, and medical resources in sustainable cities. The advancements in Internet of Things (IoT) and ubiquitous computing have brought us a smart city, where we believe the controllable and networked city infrastructures (e.g., transportation tools, buildings, public exercise facilities) can be employed to assist disease transmission detection, treatment monitoring, and rehabilitation management. Furthermore, artificial intelligence (AI) empowered healthcare has proven to be more efficient, more affordable, and more personalized. Therefore, it is highly necessary to aggregate AI technologies to healthcare in the context of smart cities.
Nowadays, cities are becoming smart and can be efficiently managed via a variety of infrastructures and facilities. Its potential to support smart healthcare systems can penetrate different occasions (e.g., smart homes, community health centres, and smart hospitals) and scenarios (e.g., abnormal behavior monitoring, disease prevention and diagnosis, clinical decision-making, prescription recommendation, rehabilitation, and postmarking surveillance). In this Research Topic, we highly appreciate the contributions investigating any of the multifaceted aspects regarding smart city assisted healthcare. More specifically, we briefly provide several opening issues as examples: In terms of disease monitoring and prevention, it is promising to leverage the infrastructures deployed at every corner of the city (e.g., building doors, street trash can, and elevators) to detect disease symptoms (e.g., cough, fever, and asthma) among certain populations. In light of actionable health decision-making, powerful deep learning algorithms and frameworks (such as convolutional neural networks and graph neural networks) can be deployed in smart health centres to analyze large-scale health data, discover distinguishable temporal/spatial/topological patterns, and support precise diagnosis. Further, building a smart healthcare framework among different parties is crucial for efficient medical services. City path planning for emergency healthcare is critical to timely treatment.
We encourage researchers and practitioners to submit any form of original research (articles, reviews, mini reviews, perspectives, short communications, general commentaries, etc.) on topics including, but not limited to, the following:
• Healthcare data collection in smart and sustainable cities
• Deployment and maintenance of city infrastructures for smart healthcare
• Sense-able city infrastructures
• Path planning for healthcare vehicles (e.g., ambulances)
• Modeling healthcare data in smart cities
• AI-assisted disease monitoring, prevention, diagnosis, and treatment in urban environments
• Multi-modality healthcare data processing and analysis
• Data mining regarding healthcare information
• Personal healthcare system design, deployment, and commentaries for smart cities
• City facility/infrastructure assisted healthcare systems
• Environment sensing to guarantee healthy living conditions
• AI-powered administration, prescription, drug safety, and surveillance in urban hospitals
• Graph representation learning in the context of healthcare
• COVID-19-related data analysis in urban environments
• Algorithms and frameworks for medical data analysis
• Design and development of portable healthcare devices/products
• Security and privacy in healthcare in smart cities
• Disease transmission and contact tracing in smart cities
Keywords: AI, healthcare, smart cities, IoT, smart hospital, machine learning, deep learning, neural networks, disease diagnosis, remote diagnosis, ubiquitous sensors, large-scale data analysis, security
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.