About this Research Topic
In recent years, there has been a rise in patient data collection. Accordingly, there is a consequent increase in the complexity of the analysis. In this regard, artificial intelligence (AI)-based data analysis has been increasingly applied in the healthcare sector. Several types of AI are already employed by healthcare providers and pharmaceutical and biomedical companies. In many cases, AI-based platforms have already been implemented. Some key applications include disease diagnosis, automated treatment recommendations, patient medication adherence, patient engagement, and hospital administrative activities. Among the various AI technologies, deep learning has evolved as one of the advanced AI tools with versatile applications, including cardiac disease diagnosis, morphometric analysis, diabetes management, etc.
Challenges:
There is no doubt that the application of AI can revolutionize the healthcare sector as we perceive it today. However, their adoption in daily clinical practice is still a challenge. This is due to the absence of proper approvals from government agencies and the training of clinicians, to name a few. Further, many healthcare acceptors still rely upon human intervention rather than interacting with automated systems. Due to these reasons, the implementation of AI tools has been greatly hampered. However, AI-based tools are expected to be adopted extensively in the healthcare sector in the next decade or so. Their implementation can revamp the healthcare experience. In this regard, deep learning-based AI tools will have the upper hand due to their significantly better accuracies than the other AI algorithms.
Aims of the special issue:
In the last decade, there has been a phenomenal increase in the applications of AI technologies in biomedical signal processing. Nowadays, all types of healthcare organizations are looking toward the different utilities of AI to provide better patient support, reduce treatment costs, and improve treatment efficiency. This has allowed for the sophistication of healthcare services in various aspects. In this regard, deep learning has helped bring a quick transformation in healthcare services by providing better classification accuracy. This special issue will concentrate on the applications of deep learning in various aspects of medical signal processing.
Potential topics to be covered:
The special issue would essentially cover, but is not limited to, the following topics:
• Deep learning for medical device development
• Deep learning for disease diagnosis
• Deep learning for arrhythmia classification
• Deep learning-based biomedical signal classification for COVID-19 and other pandemic diseases
• Generating biomedical signals (e.g., ECG, EEG, EMG, etc.) by deep learning
• Deep learning models for denoising biomedical signals (e.g., ECG, EEG, EMG, etc.)
• Deep learning of feature representation for biomedical signal analysis
Keywords: Deep learning, AI-based analysis, biomedical signal processing
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