Multiple Diseases Forecast Through AI and IoMT Techniques: Systematic Literature Review

Over the past few years, researchers and developers have managed to overcome several challenges in order to provide informative, interactive and effective healthcare solutions. In particular, the recent developments in Artificial Intelligence (AI) field, more specifically ML and DL techniques, have contributed significantly to making Clinical Decision Support Systems (CDSS) more effective in healthcare processes by improving diagnostics, therapy, and prognosis. On another side, the Internet of Medical Things (IoMT), which has evolved into a tool to next-generation bioanalysis., combines networked biomedical devices with software applications to efficiently support healthcare tasks. Practically speaking, persons are susceptible to suffer from one or more chronic or non-chronic diseases under several conditions. This is why AI and IoMT are believed to enable the early identification of potential threats to human health that require effective health actions. In this paper, we accomplish an SLR of AI-based CDSS and IoMT techniques for multi-disease forecasting by making analysis and discussions according to various aspects. The aim is to help researchers in this field of interest to open up future prospects, especially since the existing literature reviews on medical decision support systems mainly focus on the prediction of a single disease rather than multiple diseases.