Impact of Cloud Computing and Machine Learning Techniques in Clinical Diagnosis: A Systematic Review

Abstract

Cloud computing and machine learning (ML) has recently gained prominence in the healthcare industry. Machine learning and cloud computing play a dynamic role in illness detection, but mainly among those living in rural locations with few medical resources. Machine-learning-based diagnosis systems function as secondary readers, assisting radiologists in accurately identifying illnesses, while cloud-based systems can enable telehealth and remote diagnostics.Artificial neural networks techniques have piqued many academics who want to investigate their potential for illness detection. An extreme learning machine (ELM) is a type of artificial neural network (ANN) with a lot of potential for handling classification issues. Cloud computing platforms provide a more precise and real-time forecast of the disease's growing tendency. Patients with life-threatening illnesses such as cancer, diabetes, neurological disorders, coronary heart disease, and HIV/AIDS are more susceptible to severe complications. This necessitates creating a solid mathematical foundation for tracking its spread and the automation of monitoring tools for dynamic online decision-making.Innovative solutions are needed to create, manage, and analyse massive data on the growing network of infected patients, patient information, and community movements and combine clinical trials, pharmaceutical, genetic, and public health data. Hence, ML can be used to manage enormous amounts of data and intelligently forecast disease spread. Cloud computing is utilised to improve the prediction process quickly by using high-speed computations.Cloud computing is gaining traction to solve the problem of delivering sophisticated services and data via the Internet.In addition, the current paper is conducted to understand the impact of cloud computing and machine learning Techniques in clinical diagnosis. The research design has been used in the present study is descriptive. Secondary data is analysed for literature review and purpose of analysis to get a precise conclusion.