###### Abstract
DL models also struggle with generalization and bias, performing well in specific contexts while failing in others [27]. The lack of interpretability, where models act as 'black boxes', continues to weaken trust among healthcare professionals [28]. Furthermore, integration with healthcare systems poses interoperability challenges for EHRs, potentially disrupting workflows. In high-pressure medical settings, models need to deliver real-time predictions, as the IoMT and DL can automate critical tasks like patient monitoring and drug dispensing. Ultimately, predictive analytics from IoMT data can significantly enhance resource allocation in areas like surgery and ICU. In this context, the Internet of Medical Things (IoMT) is a transformative ecosystem that integrates medical devices, software, hardware platforms, and networks to collect, process, and share healthcare-related data. This interconnected ecosystem not only enables efficient and real-time healthcare delivery but also supports personalized treatment by leveraging advanced technologies, including sensors, communication networks, edge devices, and cloud platforms. Together, these components establish the backbone of the IoMT's architecture, driving a patient-centric approach to modern healthcare systems. Integrating deep learning (DL) with the IoMT [29] will allow enormous data flows, such as high-resolution medical images and real-time data collected by sensors. The data need to be transformed into usable insights to enhance health service quality, scalability, and accessibility. Transmitting raw data across network systems can lead to traffic congestion, data overflow, long latency, etc. DL models can be deployed to locally process data, perform preliminary analyses, detect abnormalities, and transmit only the results instead of the entire dataset. This reduces network load and ensures efficient data transmission without compromising the system's performance. We reviewed related papers through GoogleScholar, IEEE, and MDPI. We focused on investigating the performance of DL applications in smart healthcare from the IoMT perspective. Additionally, we classified them based on the three dimensions of the problem space and the structured framework. Furthermore, we explored the challenges and opportunities regarding DL in the IoMT. Table 1 compares our survey and the other six related survey papers. Most of these surveys focus on the application layer, particularly on challenges, especially security. In fact, DL in the network layer and the perception layer is a critical component of IoMT systems. Furthermore, most of these surveys offer only a brief introduction to the DL environment despite its significance as a foundational factor in designing and implementing effective DL models. This gap highlights the necessity of addressing DL in IoMT systems from various angles in our defined problem space. To summarize, the major contributions of this survey paper are as follows:  
* Proposed problem space and framework: We present a comprehensive problem space framework that systematically classifies DL-based application scenarios in the IoMT with healthcare applications based on analyzing three orthogonal approaches. * Comprehensive survey of applications: We review a large number of applications of DL in the IoMT, including precision diagnostics, intelligent surgical tools, wearable devices for health care, and individualized therapy. * Challenges and future directions: We summarize some current challenges in DL applications for the IoMT, and then show a related future research plan. The remainder of the article is organized as follows. Section 2 briefly provides some background. Section 3 explains the main benefits and some challenges of using DL for IoMT in healthcare and the architecture of the IoMT system. Section 4 reviews the DL applied in IoMT based on our designed 3D problem space. Section 5 summarizes some existing challenges and issues in IoMT with DL applications, especially in the security of applications.