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
Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
Adrian Höhl, Ivica Obadic, Miguel Ángel Fernández Torres, Hiba Najjar, Dario Oliveira, Zeynep Akata, Andreas Dengel, Xiao Xiang Zhu
A Systematic Review of Low-Rank and Local Low-Rank Matrix Approximation in Big Data Medical Imaging
Sisipho Hamlomo, Marcellin Atemkeng, Yusuf Brima, Chuneeta Nunhokee, Jeremy Baxter
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review
Daniel Schwabe, Katinka Becker, Martin Seyferth, Andreas Klaß, Tobias Schäffter