AI-Powered Innovations to Enhance Kidney Disease Detection and Patient Interaction

Abstract

Kidney disease is a serious worldwide health concern that can be improved by early detection and efficient monitoring. Convolutional neural networks (CNNs), a recent development in artificial intelligence (AI), have greatly improved the precision of kidney disease segmentation and classification in medical imaging. Furthermore, conversational AI has become a useful tool for supporting patient interaction and early diagnosis. 40 research studies on CNN-based renal illness classification, semantic segmentation methods, and AI-driven chatbots for kidney disease detection are thoroughly examined in this review paper. We emphasize the advantages, disadvantages, and performance indicators of different datasets, preprocessing strategies, feature extraction approaches, and deep learning architectures. According to the results, CNN-based models such as U-Net, ResNet, and Vision Transformers have segmented kidney structures with remarkable accuracy, and hybrid AI chatbots that combine machine learning and natural language processing show promise in supporting clinical decision-making. But there are still issues like model generalizability, dataset constraints, and ethical issues. In addition to pointing out current research gaps, this study offers suggestions for future paths, such as better explainability of AI models, multimodal data integration, and improved chatbot interactions for individualized healthcare.

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