Computational intelligence is a powerful methodology for addressing complex real-world problems when mathematical or traditional modeling can have limited use. Computational intelligence paradigms have numerous branches, including neural networks, swarm intelligence, expert systems, evolutionary computation, fuzzy systems, and learning systems. Such systems can play a vital role in handling the different aspects of healthcare.

With the advances in Medical Internet of Things (MIoT), it has become feasible to design numerous automated diagnostic models for early-stage diagnosis of different diseases. Machine learning models have been broadly employed to evaluate biomedical data collected from MIoT. However, these models need effective feature extraction and selection approaches as a pre-processing tool to evaluate the collected biomedical data. As such, the feature extraction and selection approaches may hinder the performance of machine learning-based biomedical data analysis. Therefore, deep learning models have been implemented to evaluate the biomedical data collected from MIoT. These models apply numerous filters at each layer to automatically extract and select the potential features of biomedical data. These models have achieved considerably better results than machine learning models, but the model building process is extremely expensive, both in terms of resources and time. Additionally, deep learning models perform efficiently when a huge amount of data is available.

The purpose of this Special Issue is to determine the new advancements and applications of deep learning models and intelligent systems for MIoT applications. We welcome original research and review articles covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in computational intelligence for Medical Internet of Things (MIoT) applications.

Potential topics include but are not limited to the following:

• Healthcare informatics
• Machine intelligence techniques for Medical Internet of Things (MIoT)
• Recognition and optimization methods for MIoT
• Soft computing approaches for MIoT
• Medical imaging and pattern recognition for MIoT
• Biomedical imaging and image processing for MIoT
• Pattern mining algorithms for biological problems
• Artificial intelligence and pattern recognition technologies for recommendation for MIoT
• Deep learning and machine learning algorithms for efficient indexing and retrieval in medical imaging
• Computational intelligence for data analysis and pattern recognition
• Internet of medical things platform for e-health applications