Comparative Review of Artificial Intelligence Driven Deep Learning in Machine Vision, Healthcare, and Memory Processing Applications

A comparative analysis of three high-impact domains are machine vision and robotic vision, healthcare prediction systems, and memory processing for deep neural networks (DNNs), which have the advancements of deep learning and machine learning. Object detection, human action recognition, and semantic segmentation are the applications of machine and robotic vision, which are becoming more precise due to advanced neural architectures. Machine learning provides robust tools, particularly in healthcare and in pregnancy, for improving maternal outcomes by predicting preeclampsia. Meanwhile, to address the traditional memory bottlenecks in DNN processing, an Artificial intelligence-driven technology called processing-in-memory (PIM) can be used, resulting in faster computation and increased energy efficiency. This comparative analysis works on applications, algorithmic structures, performance metrics, hardships, and future directions of the machine and robotic vision, healthcare, and memory processing of AI are also extensively discussed systematically. The novelty of this work depends on its interdisciplinary approach, bringing together advances in machine vision, healthcare, and PIM to address common challenges like dataflow management, interpretation of models, and energy efficiency. It mainly focuses on cross-domain solutions and emerging trends, providing a unique viewpoint on long-term and scalable AI applications across a wide range of fields.