AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
This systematic review, conducted using the PRISMA framework, analyses existing studies to examine AI applications in healthcare, focusing on methodologies, datasets, performance, and challenges.
This comprehensive project focuses on Multi-Disease Detection, employing a diverse set of algorithms, encompassing deep learning, standard machine learning, transfer learning, and hybrid models such as VGG-19, ResNet50, Random Forest, and Gradient Boosting.
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for i...
It is argued that LLM-based CDSS are not supported for routine autonomous use and require clinician supervision, and set out a research agenda centred on validation, governance and human-in-the-loop deployment.
This review systematically explores deep learning‐driven advancements, focusing on their theoretical foundations, key technological breakthroughs and typical application scenarios, and systematically categorizes fusion architectures into data‐level, feature‐level, and decision‐level hierarchies across diverse modalitie...
The research introduces a dimension in which medical specialists can have real-time conversations with a trained LLM model that leverages knowledge from a medical encyclopedia, and enhances collaboration between AI and medical professionals, creating a platform for exchanging knowledge.
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