Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Abstract

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

“… Range of transmission: when the range of data transmission is short, having postural body movements leads to disconnection and repartitioning among sensor nodes in the IoMT system [ 5 ]. Heterogeneous environment: the routing protocol for SHS must be capable of handling challenges because of the heterogeneous environment of BSN applications (for example DexterNet) [ 83 , 95 ]. QoS: when we deal with real-time BSN applications, such as ECG, it is very sensitive for data loss, and it is time critical [ 96 ].…”

A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis

“…Table 1 summarizes the recently published deep learning methods. There are some recent deep learning methods which have been used for the detection of different diseases that performed well [ 60 – 67 ].…”

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

“…The Healthcare industry across the globe has evolved extensively with the advent of machine intelligence. Nasr et al [ 3 ] explore current state-of-the-art smart healthcare systems, highlighting significant topics such as wearable and smartphone devices for fitness monitoring, ML for illness prediction, and assistive frameworks, including social robots designed for assisted living environments. Bharadwaj et al [ 4 ] confer applications of ML algorithms integrated with the Healthcare Internet of Things (H-IoT) in terms of their compensations, choice, and potential future aspects.…”