Artificial Intelligence, Sensors and Vital Health Signs: A Review

Abstract

Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions.

Vital signs and medical emergency response (MER) activation predict in‐hospital mortality in general surgery patients: a study of 15 969 admissions

“…14 For the consideration of technologies to assist with monitoring of general surgery patients, human factors must be considered and responsibility shared. 15 Given high accuracy in anticipating deterioration, nurse monitoring is likely to retain an important role in observations on inpatient surgical wards. 16 However, during any future implementation, it must be acknowledged that the success of composite outcome measures and early warning systems depends on the reliability of the database from which they come.…”

“…Novel strategies for vital sign monitoring, such as the use of head‐worn displays, could be considered 14 . For the consideration of technologies to assist with monitoring of general surgery patients, human factors must be considered and responsibility shared 15 . Given high accuracy in anticipating deterioration, nurse monitoring is likely to retain an important role in observations on inpatient surgical wards 16 .…”

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

“…Big clinical data requires data integration and analysis by connecting medical devices using an intelligent and distributed platform of sensors, effectors through (IoT) to local or cloud-based artificial intelligence/machine learning (AI/ML) systems. This approach allows the integration of data from different sources to provide earlier and more accurate diagnosis and prognosis of a patient's condition and take appropriate preventive and therapeutic actions [30][31][32] (Figures 4 and 5). Preventive medicine is based onsystems already in place that could change the face of medicine in the future, focusing on the prevention of disease and secondary changes and maintaining the best possible health (including the functional state of the user) instead of responding to a decline in health (functional state) in the form of therapeutic interven-tion [33].…”

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

“…In this respect, various sensing systems and the use of AI in medical decisionmaking are studied. The study in [12] provides coverage of the different wearable sensors for healthcare delivery, primarily from a hardware perspective, and briefly highlights the benefits and challenges of AI. More recent work [13] covers the use of AI in the internet of medical things and its different applications concerning various algorithms.…”