## 6 Opportunities  
Addressing challenges and pursuing future research directions will enable the field of DL in IoMT to continue evolving, providing more effective, reliable, and secure solutions for smart healthcare. Performance scalability and computational complexity: The implementation of DL in IoMT systems is constrained by significant challenges related to scalability and computational complexity. As IoMT applications become increasingly sophisticated, such as those involving robotic surgery, remote patient monitoring, or multimodal diagnostic systems, integrating diverse data sources and the hierarchical nature of advanced DL models create immense computational demands. Robotic surgery, for example, requires real-time processing of high-resolution imaging data, precise control of robotic instruments, and seamless integration of multiple sensors and feedback loops. These operations often require powerful hardware and substantial computational resources, which may not be readily available in resource-constrained clinical settings. In addition, scaling these models to accommodate increasing data volumes and connectivity between hospital networks or home healthcare systems presents additional challenges. Current DL architectures, while accurate, are often computationally expensive and not optimized for applications with real-time requirements. Future research must address these issues by simplifying model architectures through various optimization techniques. For instance, using model quantization and pruning to reduce memory and computation costs, as seen in MobileNet [259] for edge devices. Knowledge distillation, as in TinyBERT [260], transfers knowledge from large models to smaller ones while maintaining accuracy. Specialized hardware like Google's TPUs [261] can be leveraged to speed up tasks. Scalability can be enhanced with model parallelism, such as distributing tasks to multiple GPUs for efficient training. Federated learning can be used to enable local device training without sending sensitive data to central servers. Asynchronous training methods, such as those used in Horovod [262], reduce synchronization bottlenecks in cloud environments. To reduce complexity, SparseGPT removes redundant parameters to enhance efficiency [263]. These techniques make DL models more practical for real-world applications, from edge AI in IoT devices to large-scale cloud training for smart agriculture. Data quality improvement: A critical challenge in applying DL in smart healthcare CPSs is the lack of standardized datasets and evaluation protocols. This gap hinders the ability to systematically compare the performance of different models and studies, creating inconsistencies in reported results. For example, in atrial fibrillation detection, variations in data collection methods, labeling standards, and dataset composition often lead to discrepancies in model evaluations. Furthermore, while many DL models demonstrate impressive performance in controlled experimental settings, they frequently struggle to generalize to real-world populations because of the diversity and complexity of clinical data. Factors like demographic variations, comorbidities, and inconsistent healthcare practices exacerbate these issues, limiting the robustness and applicability of these models. To overcome these challenges, future research must prioritize the development of standardized benchmarks and datasets tailored to the unique requirements of healthcare applications. Establishing uniform data collection and annotation protocols will facilitate fair comparisons and encourage more consistent model evaluations. Furthermore, testing DL models in diverse real-world environments is essential to assess their reliability and generalizability across different populations and healthcare settings. Collaborations among academic institutions, healthcare organizations, and industry stakeholders can further accelerate the creation of comprehensive and diverse datasets.