Artificial Intelligence (AI) enables important improvements in biomedical informatics. It also helps with the future of diagnostic systems. AI can improve the treatment process for degenerative diseases (i.e., Alzheimer’s disease). AI and big data are closely integrated. Big data is the data with diversity, scale, and complexity that requires new structures, algorithms, techniques, and analytic to facilitate the management, visualization, and retrieval of hidden information.

Big data in biomedical informatics is needed to help us find novel AI methods. Machine learning and deep learning in diagnostic systems can be trained and tested on large-scale biomedical databases. These techniques can also be used and trained for treatment methods. There are many challenges in this research field. Firstly, there is a need to develop fast and reliable methods for biomedical informatics big data. Moreover, there is a need to include the optimization model for treatments. This will ensure there is an effective use of multiscale techniques for the classification and prediction of degenerative diseases. Finally, another challenge is the scientific rationale for characterizing the behaviour of the degenerative brain tissue.

The aim of this Special Issue is to bring together original research and review articles discussing artificial intelligence and machine learning for assessing Alzheimer’s disease. We welcome submissions including biomedical informatics and big data. In particular, we welcome submissions discussing the application of new methods for data processing, analysis, and knowledge discovery of biomedical and health informatics. Moreover, this Special Issue will address several challenges associated with the development of computer-aided diagnostic systems that are used for large-scale data in biomedical informatics.

Potential topics include but are not limited to the following:

• Application of healthcare informatics for Alzheimer’s disease
• Big data in Alzheimer’s disease
• Deep learning methods for classifying lesions and tissues from medical imaging data focusing on Alzheimer’s disease (e.g., ultrasound, computed tomography, and magnetic resonance imaging)
• Machine learning methods for computer-aided detection in medical imaging focusing on Alzheimer’s disease (e.g., ultrasound, computed tomography, and magnetic resonance imaging)
• Machine learning methods for segmentation, denoising, and super-resolution of medical imaging data focusing on Alzheimer’s disease (e.g., ultrasound, computed tomography, and magnetic resonance imaging)
• Biomedical imaging and pattern recognition with a focus on assessing patients with Alzheimer’s disease
• Biomechanical modelling of Alzheimer’s disease with deep learning
• Artificial intelligence used in robotic telemedicine for Alzheimer’s disease