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
Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: A Systematic Review
Khushbu Khatri Park, Abdulaziz Ahmed, Mohammed Ali Al-Garadi
Not Only WEIRD but "Uncanny"? A Systematic Review of Diversity in Human-Robot Interaction Research
Katie Seaborn, Giulia Barbareschi, Shruti Chandra
Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications
Marco Kemmerling, Daniel Lütticke, Robert H. Schmitt
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review
João Vitorino, Tiago Dias, Tiago Fonseca, Eva Maia, Isabel Praça