The need for multilingual biomedical databases was already pointed out by different authors. They argue about the need for making translations available in other languages and centralized access to regional databases and that one should not disregard citations in other languages. This fact could not be any more real in the current situation regarding the novel coronavirus. When considering treatment, diagnosis and prevention, around 44% of the articles in PubMed were written in Chinese. This prompts the urgent need for quality automatic translation to make such extremely valuable information available to medical personnel in as many languages as possible. We also point out that the community should also make efforts to guarantee editorial quality and to follow the best practices in editing and publishing. This is of critical importance as well, such that the content is properly scrutinized before being published.
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http://dx.doi.org/10.1016/j.ijid.2020.05.023 | DOI Listing |
Brain Sci
December 2024
School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA.
Background: Cognitive impairment poses a significant global health challenge, emphasizing the critical need for early detection and intervention. Traditional diagnostics like neuroimaging and clinical evaluations are often subjective, costly, and inaccessible, especially in resource-poor settings. Previous research has focused on speech analysis primarily conducted using English data, leaving multilingual settings unexplored.
View Article and Find Full Text PDFStroke
January 2025
Center for Brain Recovery, Boston University, MA (M.J.M., E.C., M.S., M.R.-M., S.K.).
Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.
Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.
Front Public Health
January 2025
University of Waterloo, Waterloo, ON, Canada.
With the increase in international migration, the need for an equitable healthcare system in Canada is increasing. The current biomedical model of healthcare is constructed largely in the Eurocentric tradition of medicine, which often disregards the diverse health perspectives of Canada's racialized immigrant older adults. As a result, current healthcare approaches (adopted in the US and Canada) fall short in addressing the health needs of a considerable segment of the population, impeding their ability to access healthcare services.
View Article and Find Full Text PDFJ Nutr
December 2024
Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan. Electronic address:
Background: While large language models like ChatGPT-4 have demonstrated competency in English, their performance for minority groups speaking underrepresented languages, as well as their ability to adapt to specific socio-cultural nuances and regional cuisines, such as those in Central Asia (e.g., Kazakhstan), still requires further investigation.
View Article and Find Full Text PDFInteract J Med Res
November 2024
Unity Health Toronto, St. Michael's Hospital, Interventional Psychiatry Program, Toronto, ON, Canada.
Background: Depression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations.
Objective: This literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases.
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