## 6 Opportunities
Every patient has unique physiological patterns affected by factors like age, genetics, lifestyle, and underlying health conditions. Standardized models often fail to account for these variations, leading to suboptimal performance in detecting anomalies or managing chronic conditions. Personalized models, trained or fine-tuned on individual patient data, can significantly improve detection accuracy, provide more reliable health insights, and facilitate targeted interventions. Future research should study techniques to personalize DL models like transfer learning, where pre-trained models are fine-tuned on patient-specific data, or adaptive learning, which allows models to evolve continuously as new data become available. Leveraging advanced methods like federated learning can also enable personalization while preserving data privacy, ensuring sensitive health information remains secure. Moreover, integrating data from multiple sources, such as photoplethysmography (PPG), electrocardiography (ECG), and wearable sensors, can enhance model accuracy and robustness. Data fusion techniques enable the combination of complementary signals, providing a substantial understanding of a patient's health. For instance, combining PPG and ECG data can improve heart rate variability analysis, while integrating accelerometer data can provide deeper insights into activity and posture changes. These approaches not only improve health monitoring precision but also pave the way for developing genuinely personalized and holistic healthcare solutions. Cross-domain applications and adaptive learning: Applying advanced AI techniques like deep reinforcement learning (DRL) [269] beyond healthcare opens up new possibilities for leveraging cross-domain insights in areas like industrial automation and smart cities. Also, the systems need to be operated via future wireless communications that integrate with sensors [270], aiming to providing sensing capability using wireless communication signals for the sake of cost reduction. By exploring how AI-powered solutions perform in diverse domains, researchers can identify strategies and innovations that can be adapted to healthcare, enriching the field with novel approaches. For instance, DRL, widely used in robotics and logistics for optimizing processes, can be adapted for tasks like robotic surgery or workflow optimization in hospitals. Similarly, predictive maintenance models from industrial automation can inform healthcare IoMT systems to proactively address device failures, ensuring seamless operation in critical care settings. Adaptive learning is another promising area, allowing DL models to adjust to continuously evolving conditions. In healthcare, environments are dynamic, with changes in patient demographics, disease prevalence, and treatment protocols, requiring models that can adapt in real time. Techniques like online learning, where models update their knowledge as new data arrive; meta-learning, which enables models to generalize to new tasks quickly; and transfer learning, to reuse the existing learning model can significantly enhance resilience and applicability [271; 272]. Adaptive models are particularly valuable in applications such as personalized health monitoring, where physiological patterns evolve over time, or in smart cities, where environmental and infrastructure conditions change dynamically. Future research should emphasize combining cross-domain applications with adaptive learning to create robust, scalable, and versatile AI systems. These systems can bridge gaps across industries while maintaining the flexibility required to address the specific challenges of dynamic environments like healthcare, enhancing both resilience and impact. Explainable AI in IoMT: DL models are frequently considered as \"black boxes\", making it challenging for healthcare professionals to gain insights into what is behind predictions or decisions.