Systematic Review
Systematic reviews synthesize existing research on a specific topic to provide a comprehensive and unbiased overview, guiding future research and informing practice. Current research focuses on applying systematic review methodologies across diverse fields, leveraging machine learning and large language models to automate tasks like literature searching, data extraction, and analysis, particularly in areas like healthcare, finance, and AI development. This approach enhances the efficiency and scalability of evidence synthesis, leading to more robust conclusions and improved decision-making in various scientific and practical domains.
Papers
Generative Artificial Intelligence: A Systematic Review and Applications
Sandeep Singh Sengar, Affan Bin Hasan, Sanjay Kumar, Fiona Carroll
A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligence
Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md. Mehedi Hasan Shawon
The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets
Zehui Yu, Indira Sen, Dennis Assenmacher, Mattia Samory, Leon Fröhling, Christina Dahn, Debora Nozza, Claudia Wagner
Differentially Private Federated Learning: A Systematic Review
Jie Fu, Yuan Hong, Xinpeng Ling, Leixia Wang, Xun Ran, Zhiyu Sun, Wendy Hui Wang, Zhili Chen, Yang Cao
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review
Nina Moutonnet, Steven White, Benjamin P Campbell, Saeid Sanei, Toshihisa Tanaka, Hong Ji, Danilo Mandic, Gregory Scott
SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety
Paul Röttger, Fabio Pernisi, Bertie Vidgen, Dirk Hovy