A literature review identified key technologies, methodologies, algorithms, and models for smart health systems, highlighting the role of IoT and AI in clinical decision support.
Smart Health indicates the use of new technologies in the healthcare sector. Literally it means “intelligent health”, and the intelligence referred to is digital, guaranteed by innovative tools such as Internet of Things (IoT) devices, communication technologies, cloud computing, artificial intelligence (AI) and big data. Thanks to sensors and devices connected to patients, such as technologically advanced bracelets and watches, it is possible to collect data on the state of health of people and treat them, even remotely, anticipating critical situations before they occur. The use of IoT to support healthcare leads to suitable recommendations and set the best policies for improving the quality of patients life, assisting practitioners and healthcare providers in decision making, collecting and exchanging information, helping to prevent events, such as a heart attack or an illness. This is possible thanks to the use of AI, which process immense amounts of data to anticipate future events. In recent years, AI based on deep learning has sparked tremendous global interest and is impacting also in healthcare. Deep learning has been widely adopted in image recognition, speech recognition and natural language processing. It could be the vehicle for translating big biomedical data into improved human health. The paper presents a literature review conducted to determine the most important technologies, methodologies, algorithms and models for smart health systems. In addition, the main application areas and challenges of smart health were explored.
Comito et al. (Wed,) conducted a review in Smart health systems. Smart health technologies was evaluated. A literature review identified key technologies, methodologies, algorithms, and models for smart health systems, highlighting the role of IoT and AI in clinical decision support.