Artificial Intelligence (AI) has emerged as a revolutionary technology in the current healthcare setting that allows for efficient disease diagnosis, prediction, and patient management. This review article offers an extensive review of AI-based approaches like Machine Learning (ML) and Deep Learning (DL) for the diagnosis of significant diseases such as cancer, diabetes, cardiovascular diseases, neurological disorders, and infectious diseases. The article reviews research articles published on significant scientific portals that strictly adhere to the PRISMA protocol that focuses on image data, Electronic Health Records (EHER), genomics information, and wearable sensor information. Other performance parameters like accuracy, sensitivity, specificity, Area Under the Curve (AUC), precision, recall, and F1 measure have been discussed for an understanding of the effectiveness of the approach. The article also focuses on the use of AI-based smart healthcare solutions and Internet of Things (IoT)-related devices that monitor diseases on a real-time basis. Nonetheless, despite the significant progress that AI has shown, issues of data privacy and the scarcity of data pose challenges.
Mr. M.I. Yasar Ihdisham1*, Mr. R. Jagadesh1, Mr. R. Dhanush2, Mr. M. Premkumar2, Mr. M. Praveenkumar3, Mr. C. Jothimanivannan3 (Sun,) studied this question.