Scoping Review
Scoping reviews systematically map the existing literature on a specific research topic, identifying key themes, gaps, and methodological approaches. Current research heavily utilizes scoping reviews to explore the applications and ethical implications of large language models (LLMs) across diverse fields, including healthcare, education, and gaming, often focusing on model performance, bias, and data limitations. These reviews are crucial for informing future research directions and guiding the responsible development and deployment of AI technologies, particularly in sensitive areas like healthcare where ethical considerations are paramount. The resulting insights contribute significantly to both the scientific understanding of these technologies and their practical implementation.
Papers
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
Siqi Li, Xin Li, Kunyu Yu, Di Miao, Mingcheng Zhu, Mengying Yan, Yuhe Ke, Danny D'Agostino, Yilin Ning, Qiming Wu, Ziwen Wang, Yuqing Shang, Molei Liu, Chuan Hong, Nan Liu
Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Tasks
Philipp Kohl, Yoka Krämer, Claudia Fohry, Bodo Kraft