## 2 Overview of Modern Technologies and Frameworks in Smart Healthcare Systems
### Artificial Intelligence in Smart Healthcare Systems  
The healthcare industry requires intelligent and predictive services, providing endless opportunities for precise and impactful patient care and administrative processes [34]. Moreover, with the incorporation of IoT devices, a colossal amount of data are generated and transmitted from different entities in the healthcare industry. The generated data needs efficient management and data improvisation by incorporating AI technology in the healthcare sector. The application of AI technology in the healthcare sector offers numerous advantages over the conventional healthcare system that uses cumbersome data analytics and decision-making techniques. It interacts with medical data (training data) to provide valuable insights into medical diagnosis, treatment, and clinical decision support. For example, in [35], the researchers explored osteoporosis disorder (porous bones), which is generally detected by standard X-rays and magnetic resonance imaging (MRI) scans. The authors aimed to improve classification of osteoporosis patients over the ultrasound features using an AI-based feature selection technique. As a result, they achieved 71% accuracy in classifying the osteoporosis patients according to the risk of fracture. Further, the studies [36; 37] proposed an AI-based remote patient monitoring system (especially for intensive care unit (ICU) patients) that provides readmission, vital sign assessment (body temperature, pulse rate, respiratory rate), and any abnormality in the patient routine care. Their proposed model outperforms others in terms of accuracy, i.e., 67.53% for readmission and 67.40% for abnormality. [PERSON] et al. [7] presented the essential characteristics of AI in healthcare sector, where they use AI models to efficiently predict the chances of heart attacks and likelihood of getting tumours, and finding intelligent patterns from the healthcare data. They presented an AI-based lightweight and secure communication scheme for the healthcare ecosystem. They evaluated their scheme using various performance parameters, such as end-to-end delays and throughput with different simulation scenarios. They concluded that the decision tree algorithm outperforms other algorithms in terms of computation time and SVM outperforms in terms of accuracy over the existing algorithms. Then, [PERSON] et al. in [31] studied the application of AI algorithms for sustainable development. Here, they examined the individuals who are in need of an AI-based question recommender system for different scenarios. The sole purpose of their study was to strengthen the AI-based question recommendation to not only for security screening and financial services, but also for incorporation in the healthcare sector where it has potential for a number of applications. Recently, federated learning has received much attention in order to data offloading and preserving privacy in the healthcare sector [32; 38]. From this perspective, the authors of [32] proposed a federated learning-based healthcare data monitoring framework to address the problem of local data acquisition. Their framework also helps medical professionals to detect skin diseases effectively. The empirical results show that their proposed framework preserves patients' data privacy and reduces the operational cost of the medical providers. Similarly, ref. [39] reviewed the problem associated with the diverse data of healthcare, where machine learning models do not have the capability