Abstract
Our modern world is marked by rapid progress in Information and Communication Technologies (ICTs). Though there are limitations of the digital divide globally, the use of Artificial intelligence (AI) has revolutionized the healthcare industry through predictive analytics and the integration of healthcare Internet of Things (IoT) devices. Predictive healthcare analytics, integrated with explainable AI (XAI), can improve the efficiency and effectiveness of healthcare delivery. Healthcare IoT (HIoT) devices provide the data for predictive analytics and enable remote monitoring of patients. Predictive healthcare analytics can identify high-risk patients for chronic conditions and develop personalized treatment plans. AI can analyze patient data, including demographic information, medical history, and lab test results, to identify patterns and predict future health outcomes leading to better intervention. Patients at high risk for acute conditions can be helped, thus reducing the overall cost of care and improving patient prognoses. HIoT devices, provide information on patient vital signs, physical activity, and medication adherence. Wearable fitness trackers, such as smartwatches and fitness bands, provide data on physical activity and sleep patterns to identify patients at risk for chronic conditions such as heart disease. Remote monitoring devices can provide real-time data on patient vital signs, enabling healthcare professionals to monitor patients remotely within a hospital environment and care facilities and intervene as needed. A well-integrated secure ecosystem with seamless wireless connectivity can usher in innovative AI-based healthcare solution.
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Chaudhari, A., Sarode, V., Udtewar, S., Moharkar, L., Patil, L., Barreto, F. (2023). A Review of Artificial Intelligence for Predictive Healthcare Analytics and Healthcare IoT Applications. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds) Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_42
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