Narrative Review
Narrative reviews synthesize existing research to provide a comprehensive overview of a specific topic, aiming to identify key trends, gaps, and future research directions. Current research focuses on applying narrative reviews across diverse fields, employing various model architectures like graph neural networks, large language models, and diffusion models to analyze complex data and improve model interpretability and efficiency. This approach is crucial for advancing scientific understanding and informing the development of practical applications in areas such as medicine, engineering, and manufacturing.
Papers
Large language models in healthcare and medical domain: A review
Zabir Al Nazi, Wei Peng
On Robot Acceptance and Trust: A Review and Unanswered Questions
Kerstin S. Haring
Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review
Bihui Yu, Sibo Zhang, Lili Zhou, Jingxuan Wei, Linzhuang Sun, Liping Bu
Robotics Applications in Neurology: A Review of Recent Advancements and Future Directions
Retnaningsih Retnaningsih, Agus Budiyono, Rifky Ismail, Dodik Tugasworo, Rivan Danuaji, Syahrul Syahrul, Hendry Gunawan
Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency -- A Review
Vasileios Alevizos, Ilias Georgousis, Akebu Simasiku, Sotiria Karypidou, Antonis Messinis
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
Yu Lu Liu, Meng Cao, Su Lin Blodgett, Jackie Chi Kit Cheung, Alexandra Olteanu, Adam Trischler
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Yueyuan Li, Wei Yuan, Songan Zhang, Weihao Yan, Qiyuan Shen, Chunxiang Wang, Ming Yang