## 6 Opportunities
These initiatives will notonly enhance the quality of healthcare data, but will also strength the robustness, reliability, and equity of DL applications in smart healthcare. Future engineering techniques, such as principal component analysis (PCA) [264], refine datasets by selecting the most relevant attributes, improving model efficiency in financial fraud detection. Active learning, as used by self-driving car systems, prioritizes the most informative data points for labeling, minimizing annotation costs while maintaining model robustness [265]. Missing values can be handled through imputation techniques--such as using K-nearest neighbors (KNN) [266] to ensure complete and reliable datasets. Data consistency and deduplication are crucial in healthcare AI. Bias mitigation strategies, including re-sampling techniques and adversarial debiasing, promote fairness in AI. These techniques collectively improve DL models' accuracy, generalizability, and reliability, making them more effective in real-world applications. Reliability and resilience: Reliability and resilience are critical considerations for deploying DL models in smart healthcare, particularly in high-stakes and critical care environments. A persistent challenge in DL is overfitting, which occurs when the model performs well on training data but the trained model fails to generalize when dealing with new unseen data. This problem is especially problematic in healthcare care, where limited and non-representative datasets are common. Overfitting can lead to unreliable predictions, misdiagnoses, and reduced confidence in AI-assisted decision making. In addition, variability in patient demographics, medical conditions, and healthcare practices require models that can adapt and generalize effectively across diverse populations and surgical procedures. To deal with these challenges, further research should prioritize the development of advanced training techniques like data augmentation, transfer learning, and federated learning to enhance model robustness and reduce overfitting. Regularization methods and cross-validation strategies can further improve model generalization. Creating synthetic data through generative adversarial networks (GANs) can also help supplement limited datasets, providing more diverse training samples. Robust testing in various demographic and clinical scenarios is also essential to assess the safety and effectiveness of DL models in real-world settings. Ensuring reliability and resilience in DL applications will not only improve patient outcomes but also foster trust in AI-driven smart healthcare systems. Advanced AI algorithms and techniques: Emerging research trends in DL highlight the potential of federated learning to enhance data privacy and security in smart healthcare by training models in decentralized data sources [267; 268]. In addition, there is a growing need for explainable AI models that can provide transparent and interpretable predictions, particularly in medical diagnostics and patient care. Future research should explore federated learning and develop methods for explainable AI to enhance the transparency and trustworthiness of DL models in smart healthcare. Federated learning enables IoMT devices to train DL models in a collaborative way without sharing raw data, thus preserving patient privacy and security. Lightweight DL models deployed on IoMT devices facilitate local data processing, which reduces latency, as seen in features such as a smartwatch that detects arrhythmias. Additionally, DL-powered IoMT devices assist elderly and disabled patients with tasks such as fall detection and medication reminders, adapting treatments based on monitored health conditions such as tremors in Parkinson's patients. Personalized models and data fusion methods: Developing personalized ML models tailored to individual patient characteristics holds immense potential for improving the precision and reliability of health monitoring systems.