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| No | References | Study design | Objectives | Findings | Limitations |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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