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The integration of fog computing into healthcare promises significant advancements in real-time data analytics and patient care by decentralizing data processing closer to the source. This shift, however, introduces complex regulatory, privacy, and security challenges that are not adequately addressed by existing frameworks designed for centralized systems. The distributed nature of fog computing complicates the uniform application of security measures and compliance with diverse international regulations, raising concerns about data privacy, security vulnerabilities, and legal accountability. This review explores these challenges in depth, discussing the implications of fog computing's decentralized architecture for data privacy, the difficulties in achieving consistent security across dispersed nodes, and the complexities of ensuring compliance in multi-jurisdictional environments. It also examines specific regulatory frameworks, including Health Insurance Portability and Accountability (HIPAA) in the United States, General Data Protection Regulation (GDPR) in the European Union, and emerging laws in Asia and Brazil, highlighting the gaps and the need for regulatory evolution to better accommodate the nuances of fog computing. The review advocates for a proactive regulatory approach, emphasizing the development of specific guidelines, international collaboration, and public-private partnerships to enhance compliance and support innovation. By embedding privacy and security by design and leveraging advanced technologies, healthcare providers can navigate the regulatory landscape effectively, ensuring that fog computing realizes its full potential as a transformative healthcare technology without compromising patient trust or data integrity.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392028 | PMC |
http://dx.doi.org/10.7759/cureus.66779 | DOI Listing |
PNAS Nexus
January 2025
Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervention target sets at a very fine-grained level; these also permit the network to be updated in response to nonpharmaceutical interventions, such as school closures.
View Article and Find Full Text PDFInt J Med Robot
December 2024
School of Mechanical Engineering, Tianjin University, Tianjin, China.
Background: In order to achieve spatial registration for surgical navigation, a spatial registration method based on point cloud and deep learning is proposed.
Methods: Neural networks are used to register medical image point clouds and patient surface point clouds to complete spatial registration of surgical navigation. An image processing method is designed to convert medical images into point clouds, and a structured light robot is used to extract patient surface point clouds.
Lancet Digit Health
December 2024
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK; Centre for Patient Reported Outcomes Research, School of Health Sciences, College of Medical and Dental Sciences, Birmingham, UK; University of Birmingham, Birmingham, UK. Electronic address:
Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups.
View Article and Find Full Text PDFGenome Biol
December 2024
Department of Computer Science, Columbia University, New York, USA.
Cloud computing allows storing the ever-growing genotype-phenotype datasets crucial for precision medicine. Due to the sensitive nature of this data and varied laws and regulations, additional security measures are needed to ensure data privacy. We develop SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data.
View Article and Find Full Text PDFNeotrop Entomol
December 2024
National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt.
Apis florea bees were recently identified in Egypt, marking the second occurrence of this species on the African continent. The objective of this study was to track the distribution of A. florea in Egypt and evaluate its potential for invasive behaviour.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!