This project seeks to devise novel algorithms and techniques leveraged in healthcare to guarantee data privacy in AI-powered systems. To bolster its credibility, the study review presents various modern approaches and technologies used to preserve data privacy of healthcare data. The project conducted an empirical study of the current development in healthcare regarding AI privacy protection to compile a steadfast literature on the subject.
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| http://dx.doi.org/10.3233/SHTI240640 | DOI Listing |
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Development and implementation of an electronic health record system for use in humanitarian emergencies, disaster response, and conflict zones.
PLOS Glob Public Health
January 2025
Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Humanitarian medical response to natural and human-made disasters can be complicated by high clinician, staff, and patient turnover. While electronic medical records are being scaled up globally, their use remains limited in humanitarian response settings. The Fast Electronic Medical Record (fEMR) system is an open-source electronic health record system specifically designed for use in resource-limited settings and humanitarian crises.
View Article and Find Full Text PDFPersonal, environmental and behavioral predictors associated with online fraud victimization among adults.
PLoS One
January 2025
Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Background: Online malicious attempts such as scamming continue to proliferate across the globe, aided by the ubiquitous nature of technology that makes it increasingly easy to dupe individuals. This study aimed to identify the predictors for online fraud victimization focusing on Personal, Environment and Behavior (PEB).
Methods: Social Cognitive Theory (SCT) was used as a guide in developing the PEB framework.
Background: Children who suffer from long-term illnesses, including asthma, cystic fibrosis, diabetes, or epilepsy, sometimes struggle to manage their ailments, which affects their quality of life and how often they use healthcare services.
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Fine-tuning a local LLaMA-3 large language model for automated privacy-preserving physician letter generation in radiation oncology.
Front Artif Intell
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Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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