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
Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius
A Systematic Review of Aspect-based Sentiment Analysis (ABSA): Domains, Methods, and Trends
Yan Cathy Hua, Paul Denny, Katerina Taskova, Jörg Wicker
Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models
Feng Chen, Liqin Wang, Julie Hong, Jiaqi Jiang, Li Zhou
Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review
Catalina Gomez, Sue Min Cho, Shichang Ke, Chien-Ming Huang, Mathias Unberath
Sentiment Analysis in Digital Spaces: An Overview of Reviews
Laura E. M. Ayravainen, Joanne Hinds, Brittany I. Davidson