Introduction: The use of open source technologies to create collaboration platforms can produce huge advantages with small investment.
Materials And Methods: We set up a telemedicine network for a healthcare district with typical centralization issues of developing countries. Our network was built using broadband Internet connection, and the digital divide in rural areas was reduced by means of wireless Internet connection. A software infrastructure was deployed on the network to implement the collaboration platform among different healthcare facilities.
Results: We obtained an integrated platform with modest investment in hardware and operating systems and no costs for application software. Messaging, content management, information sharing, and videoconferencing are among the available services of the infrastructure. Furthermore, open source software is managed and continuously updated by active communities, making it possible to obtain systems similar to commercial ones in terms of quality and reliability.
Conclusions: As the use of free software in public administration is being widely promoted across the European Union, our experience may provide an example to implement similar infrastructures in the field of healthcare and welfare.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1089/tmj.2013.0239 | DOI Listing |
PLoS One
December 2024
Department of Curriculum & Instructional Technology, Faculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia.
The progress of open source technology is inseparable from cultivating open source talents in universities. The combination of open source communities and university education can cultivate students' practical innovation abilities. Currently, we are facing problems such as the shortage of open source talents and the sustained use of open source technologies by open source talents.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
JAMA Netw Open
December 2024
Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Importance: Radiotherapy (RT) plan quality is an established predictive factor associated with cancer recurrence and survival outcomes. The addition of radiologists to the peer review (PR) process may increase RT plan quality.
Objective: To determine the rate of changes to the RT plan with and without radiology involvement in PR of radiation targets.
Indian J Ophthalmol
December 2024
Department of Retina and Vitreous Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Coimbatore, Tamil Nadu, India.
The pupillary direct and consensual reflex is an important non-invasive quick assessment of the neurological state of the eye. Currently, there is no cheap and affordable recording tool for screening and documentation of a relative afferent pupillary defect. We describe how to construct a frugal, do-it-yourself handheld scotopic binocular pupillometer device called Pupilmate.
View Article and Find Full Text PDFJ Xenobiot
December 2024
Pharmacy Department, CEU Cardenal Herrera University, CEU Universities C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain.
In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!