Table 2.

Summary of Findings of the Selected Studies based on connected healthcare in Smart Cities.

No	References	Study design	Objectives	Findings	Limitations
1	Hossain (2017) [18]	Quantitative study	To present a cloud-based smart healthcare monitoring model to effectively interact with the environment, different nearby smart devices, and stakeholders of smart cities for accessible and affordable healthcare	The presented method is found to be successful in achieving VPD, with an accuracy of 93%	Further research is required to validate the results and to increase the model accuracy
2	A. Kumar (2020) [32]	Quantitative study	To propose a  hybrid deep learning model to overcome the issue related to the filtration of duplicated questions in healthcare	The proposed model has shown an accuracy of 86.375%	Further research is required to validate the results and to increase the model accuracy
3	Gyrard, Amelie et al. (2016) [33]	Qualitative study	Proposed an SEG 3.0 as a methodology	Proposed an SEG 3.0 methodology to amalgamate, associate, and offer semantic interoperability	The proposed methodology was not implemented in other domains
4	G. Tripathi et al. (2020) [36]	Qualitative study	To encourage real-time analysis and to present the concept of “mobile edge computing”	The proposed model is found to be secure for executing time-bound and critical edge computations	Further research is required to use this model in healthcare systems
5	M. I. Pramanik (2017) [37]	Qualitative study	To propose a conceptual framework known as “big data–enabled smart healthcare system framework”	The results of the study can be used by healthcare systems to reinforce the strategic organisation of smart systems and complex data in the healthcare context	The framework is not practically implemented in the healthcare industry; hence, research is required to validate the results first for actual implementation
6	A. Alghamdi et al. (2021) [53]	Quantitative study	To use 2 different transfer learning methods for retraining the VGG-Net and gained 2 different networks which include VGG-mi-1 and VGG-mi-2	Results of the study showed that the VGG-MI-1 showed sensitivity, specificity, and accuracy of 98.76%, 99.17%, and 99.02%, respectively, and the VGG-MI2 model showed sensitivity, specificity, and accuracy of 99.15%, 99.49%, and 99.22%, respectively	The effectiveness of the model is validated only for ECG data
7	A. N. Navaz (2021) [39]	Review-based qualitative study	To conduct a review on smart and connected health (SCH)	Several countries have used SCH successfully for diagnosis, detection, tracking, monitoring, resources allocation, and controlling of the Covid-19 cases	There are several challenges present related to its validation and detailed research to be used all over the world
8	M. Poongodi et al. (2021) [41]	Review-based qualitative study	To explore the implication of the latest trends in connected healthcare including IoT and 5g wireless connection	IoT and 5g wireless connection can be used effectively to reduce the challenges faced by patients and the healthcare profession also in an emergency	These systems are required to validate further in real-time applications
9	Nosratabadi et al. (2019) [25]	Review-based qualitative study	To explore the needs of the extraction of big data urban population	The exploration of urban data found to be helpful to provide a key to supplement a contemporary notion of Big Data for reaching the aim of sustainable and resilient smart cities as figured out in the 11th Sustainable Development Goal	Different datasets are not compared; hence, further research is required
10	Hossain, Muhammad, and Alamri (2017) [30]	Qualitative study	To represent the state-of-the-art of deep learning and machine learning methods that can be used in real time	Results of the study showed that the identified deep learning and machine learning methods mainly addressed the issues in the main domains including urban transport, health, and energy	Deep learning and machine learning methods are found to be effective in specific domains; hence, research is required to explore every domain deeply
11	K. Shankar et al. (2021) [42]	Qualitative study	Diagnosis of COVID-19 using chest X-ray images using synergic deep learning (SDL) is proposed for smart healthcare system	The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods. Authors have shown that the classification of COVID-19 can be effectively performed by the integration of FBL and SDL. Simulation with different dataset is conducted for ensuring the effectiveness of the FBF-SDL model over the existing models and to examine the classifier outcome of the SDL model	In this paper, authors created a new synergic DL-based COVID-19 classification model with chest X-ray images. To improve the quality of the chest X-ray images, the SDL model undergoes initial processing using the FBF technique. Hence, research is required to explore every domain deeply