ABSTRACT
Because of the growing influence of data science in almost every aspect of human existence, namely, entertainment, financial services, and medical services, the health sector has started considering artificial intelligence (AI)-powered techniques in next-generation medical technology. AI is claimed to have the potential in improving every procedure in medical service. AI is anticipated to assist medical professionals with a wide range of operations, encompassing management tasks, diagnostic procedures, and dedicated assistance in sectors like image processing, machine digitalisation, and patient care. The ‘Internet of things (IoT)’ along with unlimited medical data analytics is strengthening the interaction between technological innovations and the medical community. Significant medical advancements in data interpretation in the medical sector are enabled by IoT-powered neural networks. Despite such advancement, there are quite a lot of uncertainties to be rectified in terms of quality. Applying deep learning to achieve consistent quality in crucial components such as reaction time, latency, and accuracy is the key to prospering in the healthcare analysis and interpretation of data for the medical industry. As a result, the presented work provides a comparative and comprehensive analysis to study different architectures for building a smart system using big data analytics and AI.
