###### Abstract  
Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, the practical implementation has challenges, including data quality, privacy, interoperability, and limited computational resources. This survey article provides a conceptual IoMT framework for healthcare, synthesizes and identifies the state-of-the-art solutions that tackle the challenges of the current applications of DL, and analyzes existing limitations and potential future developments. Through an analysis of case studies and real-world implementations, this work provides insights into best practices and lessons learned, including the importance of robust data preprocessing, integration with legacy systems, and human-centric design. Finally, we outline future research directions, emphasizing the development of transparent, scalable, and privacy-preserving DL models to realize the full potential of IoMT in healthcare. This survey aims to serve as a foundational reference for researchers and practitioners seeking to navigate the challenges and harness the opportunities in this rapidly evolving field. deep learning (DL); Internet of Medical Things (IoMT); DL applications; smart healthcare +
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physical space and synthesizes sensing, communication, computing, and machine learning, supporting real-time monitoring, control, and management of a variety of physical things in various application domains (energy, transportation, manufacturing, homes, healthcare, etc.) [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], in smart healthcare CPSs, missing real-time monitoring can lead to delayed responses to critical health events (e.g., heart attacks, strokes), which could otherwise be prevented with early intervention. Manual infusion pump operations increase the risk of human errors, such as incorrect medication dosing, potentially leading to adverse drug events. Similarly, a lack of precision and real-time feedback in surgical procedures may result in complications, prolonged recovery times, or suboptimal outcomes. In recent decades, technological advancements have transformed healthcare by enhancing diagnostics, treatments, and disease monitoring [18]. Innovations like EHRs, telemedicine [19], deep learning (DL) [20], the Internet of Things (IoT) [21], and robotics [22] have made care more efficient and accessible. Particularly, DL enhances medical cyber-physical systems (MCPSs) with AI, enabling patient empowerment through wearables and mobile health apps for self-monitoring. This transition fosters proactive, patient-centered healthcare driven by advanced sensing and data analytics [23]. DL excels at detecting diseases from MRI, CT, and X-ray images in medical imaging. It addresses challenges like large gigapixel histopathology scans and noisy 3D images, where poor labeling complicates training [24]. DL models analyze ECGs for early arrhythmia signs in smartwatches and fitness trackers, enhancing health monitoring and improving predictions based on heart rate and blood pressure [25]. However, DL models in MCPSs face significant challenges due to limitations in sensing, communication, control, and computing. High-dimensional data from IoMT systems often degrade their real-world performance, leading to data imbalance, noise, and mislabeling issues. Critical resources like processing power and memory are frequently limited, hindering intensive algorithm execution. Additionally, data problems are worsened by fragmentation and incompleteness, including privacy, security, and the availability of well-labeled datasets [26].