Background: Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain.
Methods: MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings.
Results: 102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated.
Conclusions: Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.
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http://dx.doi.org/10.1186/s12911-015-0145-7 | DOI Listing |
Sensors (Basel)
December 2024
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Most current research in cloud forensics is focused on tackling the challenges encountered by forensic investigators in identifying and recovering artifacts from cloud devices. These challenges arise from the diverse array of cloud service providers as each has its distinct rules, guidelines, and requirements. This research proposes an investigation technique for identifying and locating data remnants in two main stages: artefact collection and evidence identification.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science & Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks.
View Article and Find Full Text PDFPhysiol Meas
January 2025
University of Duisburg-Essen, Bismarckstr. 81 (BB), Duisburg, 47057, GERMANY.
Objective: In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself.
Approach: This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed
to address these challenges by converting Python-based AI models into platform-independent hardware description language (HDL) code accelerators.
PLoS One
January 2025
School of Humanities, Ningbo University of Finance and Economics, Ningbo, Zhejiang, China.
Lightweight container technology has emerged as a fundamental component of cloud-native computing, with the deployment of containers and the balancing of loads on virtual machines representing significant challenges. This paper presents an optimization strategy for container deployment that consists of two stages: coarse-grained and fine-grained load balancing. In the initial stage, a greedy algorithm is employed for coarse-grained deployment, facilitating the distribution of container services across virtual machines in a balanced manner based on resource requests.
View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
August 2024
Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha 410013.
Objectives: Software for pharmacological modeling and statistical analysis is essential for drug development and individualized treatment modeling. This study aims to develop a pharmacokinetic analysis cloud platform that leverages cloud-based benefits, offering a user-friendly interface with a smoother learning curve.
Methods: The platform was built using Rails as the framework, developed in Julia language, and employs PostgreSQL 14 database, Redis cache, and Sidekiq for asynchronous task management.
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