## 6 Conclusions and Future Perspectives  
Despite substantial improvements in AI models and the emergence of extensive research in healthcare applications, there is still a shortage in applying and utilizing AI models in healthcare. Many studies have explored the problem of the classification and detection of COVID-19, especially in light of the influence of the ongoing pandemic. This review shows that most prior studies of COVID-19 image classification are misleading because they used multi-class classifiers (i.e., with only three or four classes) with larger samples for the COVID-19 class compared to the samples for other classes; this gives the algorithm an incorrectly high percentage of sensitivity. In addition, most studies addressing the detection and classification of pneumonia in medical images have focused on differentiating between two or three classes, e.g., viral, bacterial, and COVID-19 infections. Despite this, the ability of AI systems to differentiate between various classes is increasing as they learn from a greater number of classes. Moreover, all approaches that have been used in the literature are based on classifying the dataset using a flat structure; only one study hasaddressed the efficiency of hierarchical classification compared to flat classification. In the literature, CNN-based classifiers have also been applied on the hierarchical ETHEC dataset. The results demonstrated that the hierarchical structure incorporated the loss function and enhanced generalization across classes. This can be attributed to the exploitation of shared features between classes at different levels, which assists in overcoming the data scarcity problem [93]. In addition, in multiple domains, classifiers in hierarchical models have been shown to reduce classification errors and break down the problem; this leads to a better performance compared to flat models [94].  
Surprisingly, several techniques have been used for the detection and classification of COVID-19, but limited research has addressed multimodal deep learning models for heterogeneous data types. It is also notable that most existing models focus on a single feature modality (i.e., medical images), while multimodal features (i.e., those combining more than one aspect of COVID-19 health information, such as medical images, diagnostic data, medication data, and laboratory data) contribute to a superior performance in disease diagnostic processes [35; 95]. Furthermore, prior studies have primarily considered the CT scan as the main radiological imaging modality for all infected cases during the ongoing pandemic. Finally, regarding the approaches mentioned earlier in this review, and taking into account its limitations, there is clearly still room to enhance and improve research in this field. By considering the gaps and exploiting the remarkable successes of each prior approach, it is expected that these can be combined into one model that may see widespread clinical adoption in the future.  
Conceptualization, S.A.A., A.A.M. and A.S.A.; methodology, S.A.A., S.A. and A.A; software, A.A.; validation, S.A.A., S.A., T.N. and A.M; formal analysis, S.A.A., S.A. and A.A; investigation, A.S.A.; resources, S.A.A., S.A. and A.A; data curation, A.S.A.; writing--original draft preparation, A.S.A.; writing--review and editing, S.A.A., S.A., T.N. and A.M; visualization, S.A.A., S.A., T.N., A.A.M. and A.S.A.; supervision, S.A.A. and A.A.M.; project administration, S.A., A.A.M. and A.S.A.; funding acquisition, S.A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors would like to thank the editor and reviewers for spending their valuable time reviewing and polishing this article. The authors declare no conflict of interest.