Guest Editors
Prof. Amin ul Haq
Email: amin@uestc.edu.cn
Affiliation: 1.School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
2.TeCIP Institute Scoula Superiore Sant’Anna (SSSA),Via Moruzzi 1, 56124, Pisa, Italy
Homepage:
Research Interests: medical data; machine learning; deep learning
Prof. Jian Ping Li
Email: Jpli2222@uestc.edu.cn
Affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Homepage:
Research Interests: convolutional neural network, model performance, artificial neural network, deep learning, Internet of medical things
Prof. Riaz Ullah Khan
Email: riazkhan@uesct.edu.cn
Affiliation: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Homepage:
Research Interests: blockchain, neuroinformatics, artificial intelligence, cyber security, big data, cyber-physical systems, complex networks, cloud computing, distributed system, and Internet of things
Prof. Yuanyuan Huang
Email: iyyhuang@hotmail.com
Affiliation: Department of network engineering, Chengdu University of Information Technology, China
Homepage:
Research Interests: multimedia, artificial intelligence and big data
Prof. Wang Zhou
Email: Dean_uestc@163.com
Affiliation: School of Computer Science and Engineering, Xihua University, China
Homepage:
Research Interests: artificial intelligence, recommender algorithm and data minin
Summary
In the Internet of Medical Things, disease diagnosis is essential for timely treatment and recovery. (IoMT). IoT is made up of various devices that are linked together via various network mediums to transmit data to a centralized server or exchange data in order to accomplish specific objectives. Similarly, IoMT can refer to a variety of medical technologies that are linked together to gather pertinent data for analysis and timely provision of prognosis, diagnosis, treatment, logistics, and other services. The success of AI in a variety of application areas is being expanded to make IoMT smarter and more intelligent in connecting medical experts, patients, and devices.
Because medical experts are humans, they may not always correctly interpret medical data such as chest X-rays, CT scans, and MRIs to diagnose the disease in its early stages. AI-based computer-aided diagnosis (CAD) systems with well-trained models diagnose diseases more accurately than medical professionals. With the appropriate IoT devices and communication infrastructure, AI-based CAD can be easily integrated into IoMT systems. This can then be used to provide services like telemonitoring, which enables medical institutions to acquire real-time medical information about the conditions of their patients.
In medical research, early disease detection is important in IoT healthcare systems and prophylactic treatments would be needed. Human lives can be spared if an accurate diagnosis is followed by prompt treatment and precaution. It is essential to validate disease biomarkers and develop novel artificial intelligence-based treatments. Computer-aided diagnosis (CAD), also known as Complementary Medicine Technique (CMT) and driven by Artificial Intelligence (AI), appears to be dependable, necessary, robust, and accurate in this regard. As a diagnostic tool, CAD/CMT is becoming more popular, especially for diseases where the standard diagnosis is painful and imprecise. Furthermore, as a result of it.
Furthermore, AI has assisted physicians in reducing diagnostic errors and improving precision. Transfer learning and federated learning methods play an important role in disease diagnosis in the IoT healthcare system. Federated learning provides a collaborative learning setting for training models on large amounts of data without requiring the data to be shared.
In return, it has saved the human body's most vital organs and decreased hospital burdens. AI has enabled the early discovery of diseases such as breast cancer, diabetes, liver and lung disease, viral diseases, Alzheimer's disease, and cardiovascular disorders. Many researchers have concentrated on illness diagnosis using AI-based diagnostic methods due to their accuracy and reliability. We encourage academics to submit original research papers as well as review articles that investigate new AI, machine learning, and deep learning, Transfer learning, and federated learning-based IoT medical diagnosis and prevention systems.
Keywords
1.Medical big data analytics and visualization
2.AI-based computer-aided diagnostic systems
3.Medical data collection and pre-processing
4.Data science and its analytic
5.New feature extraction and feature selection from medical data
6.Intelligent IoT-healthcare system
7.Designing new medical data sets
8.Critical disease diagnosis using image data
9.Data privacy using federated learning
artificial intelligence; computer-aided diagnosis; complementary medicine technique; machine learning; deep learning; signal processing; biomedical signal analysis
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