## 6 Opportunities
This lack of interpretability undermines trust and could limit the adoption of AI in critical medical applications, where transparency is essential for accountability and informed decision making. The need for explainability becomes even more urgent in the context of IoMT systems, which often deal with life-critical data such as ECG signals, oxygen levels, and patient monitoring metrics. Explainable AI techniques, including SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention mechanisms, are examples that aim to deal with this challenge. These methods offer insights into which features or data segments most influence the model's decisions, enabling healthcare professionals to validate AI-driven recommendations. For instance, in IoMT systems analyzing ECG data, explainable AI can identify and highlight specific segments of the signal responsible for detecting anomalies, such as arrhythmias, making the results more interpretable and actionable. In high-stakes applications, such as diagnosing life-threatening conditions or managing chronic diseases, explainable AI ensures that clinicians can confidently rely on AI systems without fearing hidden biases or errors. Furthermore, it enhances collaboration between AI systems and medical professionals, bridging the gap between technological advancement and practical usability. Future research should focus on integrating explainability into IoMT solutions from the outset, making sure that AI-driven healthcare systems remain transparent and trustworthy, as well as being aligned with the needs of clinicians and patients alike. Trust is a major barrier to AI adoption in healthcare, particularly in high-stakes applications, in which incorrect predictions can have severe consequences. Explainable AI (XAI) directly addresses this concern by improving transparency, interpretability, and accountability in DL-based IoMT solutions. By making AI decisions understandable, XAI fosters greater trust among clinicians, patients, and regulatory bodies. XAI enhances trust in DL applications for IoMT by improving decision transparency, bias detection, regulatory compliance, and clinician-AI collaboration. XAI techniques allow medical professionals to understand why a model made a specific prediction, reducing skepticism and improving confidence in AI-driven diagnostics. For example, in automated diabetic retinopathy detection, XAI can highlight affected retinal regions, helping ophthalmologists validate AI-based assessments. Additionally, XAI helps identify biases in AI models, which may arise from imbalanced training data. This could result in disparities in healthcare outcomes. Through offering insights into model decision making, XAI allows healthcare professionals to recognize and mitigate these biases so that fairness can be ensured. Regulatory compliance is another crucial factor, as frameworks like GDPR and HIPAA require AI-driven healthcare systems to provide explainable justifications for their decisions. XAI supports auditability, helping regulatory bodies assess and certify AI-powered medical devices for safe deployment. In remote monitoring and telemedicine, IoMT applications generate vast amounts of real-time patient data, where XAI ensures that AI models provide interpretable recommendations, allowing clinicians to make informed, confident decisions in virtual consultations. Furthermore, XAI strengthens clinician-AI collaboration by making AI decision processes transparent, enabling clinicians to combine human expertise with AI insights rather than blindly relying on automated predictions. This hybrid approach improves the adoption and acceptance of AI-driven healthcare systems. In high-stakes applications such as diagnosing life-threatening conditions or managing chronic diseases, explainable AI ensures that clinicians can trust AI outputs without fearing hidden biases or errors.