Submission Deadline: 31 May 2025
This special issue delves into the transformative impact of artificial intelligence (AI) and machine learning techniques on disease detection and classification in healthcare. Recent advancements have led to significant improvements in diagnostic accuracy and efficiency, particularly through applications involving various biomedical signals and imaging modalities. These include electroencephalograms (EEG), electromyograms (EMG), electrocardiograms (ECG), heart rate (HR) signals, computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and other medical images or videos. By leveraging these rich data sources, AI models can extract meaningful patterns and biomarkers essential for early detection and precise classification of a wide range of diseases.
The collected works in this issue focus on the development of deep learning algorithms and innovative AI methodologies tailored to process and analyze these complex biomedical data. A key emphasis is placed on explainable artificial intelligence (XAI) techniques, which provide insights into how machine learning models perform classification tasks. XAI enhances transparency and interpretability, addressing critical concerns related to clinical trust and adoption of AI systems.
We aim to showcase smart health applications that incorporate XAI-based next-generation methods, bridging the gap between sophisticated AI technologies and practical clinical implementation. By exploring the integration of these advanced tools into healthcare practices, the articles seek to optimize diagnostic workflows, improve patient outcomes, and contribute to the advancement of personalized medicine. This compilation serves as a comprehensive resource for clinicians, researchers, and practitioners interested in the latest AI-driven solutions for disease detection and classification.
This special issue delves into the transformative impact of artificial intelligence (AI) and machine learning techniques on disease detection and classification in healthcare. Recent advancements have led to significant improvements in diagnostic accuracy and efficiency, particularly through applications involving various biomedical signals and imaging modalities. These include electroencephalograms (EEG), electromyograms (EMG), electrocardiograms (ECG), heart rate (HR) signals, computed tomography ... show more
Editorial board
