Editorial: Women in AI medicine and public health 2022

Lin-Ching Chang and Anastasia Angelopoulou 2023. Editorial: Women in AI medicine and public health 2022. Frontiers in Big Data. 6 1303367. https://doi.org/10.3389/fdata.2023.1303367

TitleEditorial: Women in AI medicine and public health 2022
TypeEdited issue
AuthorsLin-Ching Chang and Anastasia Angelopoulou
Abstract

The landscape of technology has undergone a dramatic transformation with the widespread adoption and rapid advancement of artificial intelligence (AI). This evolution has had a profound impact on various sectors, reshaping not only the way industries operate but also fundamentally altering the way we perceive and interact with the world around us. In particular, AI are revolutionizing the fields of medicine and public health by offering innovative ways to analyze data, make predictions, improve patient care, and even playing central role in advancing agendas of inclusion and equality. However, gender disparity is still evident within the realms of AI, especially within the context of medicine and public health. Despite their accomplishments, women scientists continue to face gender-specific hurdles, such as navigating their public presence and cultivating secure, inclusive work environments.

This Research Topic from Frontiers in Big Data aims to promote and highlight the research work of women scientists, across the fields of AI in medicine and public health. This Research Topic is part of the Women in Artificial Intelligence series. In each work, the first author or the last author needs to be a woman researcher. Each paper underwent a rigorous review process, involving at least two reviewers and two rounds of thorough revisions before acceptance. Six articles were selected that comprise four original research, one brief research report, and one study protocol. Listed below are the papers that made important contributions to this Research Topic.

KeywordsWomen in AI
Deep Learning
Medical Data Analysis
Health Data
Machine Learning
Neural Networks
Article number1303367
JournalFrontiers in Big Data
Journal citation6
ISSN2624-909X
Year2023
PublisherFrontiers
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.3389/fdata.2023.1303367
PubMed ID37908455
Web address (URL)https://www.frontiersin.org/articles/10.3389/fdata.2023.1303367/full
Publication dates
Published13 Oct 2023

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Growing neural gas (GNG): A soft competitive learning method for 2D hand modelling
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Automatic landmarking of 2D medical shapes using the growing neural gas network
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Automatic landmark extraction from a class of hands using growing neural gas
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Delivering distance learning material via interactive television in the UK: a dynamic content-based inferface
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Evaluating statistical shape models for automatic landmark generation on a class of human hands
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diARTgnosis: study of European religious painting
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A dynamic model for delivering distance learning material via interactive television
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An extensible movie system interface for information-rich television
Angelopoulou, A., Psarrou, A. and Parapadakis, D. 2001. An extensible movie system interface for information-rich television. in: Graham, P., Maheswaran, M. and Eskicioglu, M.R. (ed.) Proceedings of the International Conference on Internet Computing, IC'2001, Las Vegas, Nevada, USA, June 25-28, 2001 USA CSREA Press.

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