About this Research Topic
Providing the correct diagnosis and assessment is a foremost thing in healthcare. It describes the patient’s health condition and supports the next level of healthcare decisions. Since it is a dynamic and variable environment, it continuously poses new challenges to medical experts and leads to diagnostic and assessment errors. Diagnostic errors can lead to adverse health outcomes, psychological distress, and financial costs. In addition, these outcomes depend on the expertise level of the professionals, and analyzing complex medical data is cognitively challenging for clinicians and experts requiring time and effort.
Over the past two decades, digital healthcare system advancements have increased drastically. AI plays a crucial role in this development. AI tools like machine learning and deep learning can study the inherent complex medical data and extract features to provide necessary outputs. AI-enabled systems can process more varied and complex data for better predictions, which aid in accurate diagnosis, brain-computer interfacing (BCI), and assessment. Therefore, incorporating advanced AI-based algorithms into the healthcare delivery system has tremendous potential to improve healthcare, specifically in automated diagnosis of neurological disorders, assessment, and therapeutic interventions. Besides, AI-based robust systems improve quality of life by reducing diagnostic errors.
This Research Topic aims to showcase robust AI-based approaches in diagnostic systems, BCI systems, decision-making in rehabilitation assessment, and automated signal processing algorithms for BCI and diagnostic techniques.
Topics include, but are not limited to, the following:
Robust BCI systems applications for neurological disorders diagnosis
Machine learning medical diagnostic technologies
Automated approaches for diagnosis of neurological disorders.
AI in rehabilitation assessment
AI-based therapeutic interventions
Signal processing (EEG, EMG) for rehabilitation
Signal processing for the analysis of EEG data
AI and machine learning early neurological assessment
Time/ Spatial frequency-based feature extraction techniques for the diagnosis of neurological disorders.
Over the past two decades, digital healthcare system advancements have increased drastically. AI plays a crucial role in this development. AI tools like machine learning and deep learning can study the inherent complex medical data and extract features to provide necessary outputs. AI-enabled systems can process more varied and complex data for better predictions, which aid in accurate diagnosis, brain-computer interfacing (BCI), and assessment. Therefore, incorporating advanced AI-based algorithms into the healthcare delivery system has tremendous potential to improve healthcare, specifically in automated diagnosis of neurological disorders, assessment, and therapeutic interventions. Besides, AI-based robust systems improve quality of life by reducing diagnostic errors.
This Research Topic aims to showcase robust AI-based approaches in diagnostic systems, BCI systems, decision-making in rehabilitation assessment, and automated signal processing algorithms for BCI and diagnostic techniques.
Topics include, but are not limited to, the following:
Robust BCI systems applications for neurological disorders diagnosis
Machine learning medical diagnostic technologies
Automated approaches for diagnosis of neurological disorders.
AI in rehabilitation assessment
AI-based therapeutic interventions
Signal processing (EEG, EMG) for rehabilitation
Signal processing for the analysis of EEG data
AI and machine learning early neurological assessment
Time/ Spatial frequency-based feature extraction techniques for the diagnosis of neurological disorders.
Keywords: Artificial Intelligence, Machine Learning, Robust Systems, Neurorobotics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
