About this Research Topic
Health care related to brain health and rehabilitation is poised to be transformed by artificial intelligence (AI). The increasing availability of brain health data combined with rapid advances in AI holds enormous promises and opportunities for transforming brain health/rehabilitation, leading to targeted diagnostics, more efficient and accessible health care, as well as personalized interventions.
Neurology and clinical neuroscience stand to benefit the most from AI-based disruptive innovations. The application of novel ML techniques to neuroimaging data has shown considerable promise in facilitating objective diagnosis of neurological disorders such as epilepsy, Parkinson’s and Alzheimer’s disease, multiple sclerosis, stroke, mild traumatic brain injury/concussion, and neurodevelopmental disorders.
Healthcare of the future will shift from care being delivered in clinical spaces to a wide range of patient-based spaces; interventions will be data-driven and based on outcomes from large multi-modal data sets; practitioners will have continuous access to patient data from wearable sensors.
We are at the start of this revolution. Within this shifting context, patients’ experience of illness, injury, and/or disability will require greater effort dedicated to providing empathetic and compassionate care. In parallel, there is an urge to build new frameworks to understand the ethical and moral implications related to patient safety, consent, autonomy, and data sharing.
Our goal is to provide a comprehensive overview of topics highlighting the opportunities and challenges of AI application to brain health and in particular to brain rehabilitation.
Healthcare and rehabilitation professionals are starting to team up with engineers, physicists, and computer scientists for Big Data use, to integrate AI-based sensing and assistive technologies into rehabilitation interventions and to develop effective and user-friendly human-computer interactions.
We encourage the submission of manuscripts emphasizing and highlighting an interdisciplinary approach.
We seek Original Research, Review, Mini-Review, Hypothesis and Theory, Perspective, Clinical Trial, Case Report, and Opinion articles covering, but not limited to, the following:
• Evidence supporting the applications of original research related to AI/Big Data for diagnostics related to brain health/rehabilitation
• Original research related to AI data visualization, sensing and assistive technologies for rehabilitation interventions
• Challenges of incorporating individual variability and client-centered care within the AI framework.
• Changing/future roles of healthcare and rehabilitation professionals
• Ethical challenges of AI on patient privacy, autonomy, data security, data management
Neurology and clinical neuroscience stand to benefit the most from AI-based disruptive innovations. The application of novel ML techniques to neuroimaging data has shown considerable promise in facilitating objective diagnosis of neurological disorders such as epilepsy, Parkinson’s and Alzheimer’s disease, multiple sclerosis, stroke, mild traumatic brain injury/concussion, and neurodevelopmental disorders.
Healthcare of the future will shift from care being delivered in clinical spaces to a wide range of patient-based spaces; interventions will be data-driven and based on outcomes from large multi-modal data sets; practitioners will have continuous access to patient data from wearable sensors.
We are at the start of this revolution. Within this shifting context, patients’ experience of illness, injury, and/or disability will require greater effort dedicated to providing empathetic and compassionate care. In parallel, there is an urge to build new frameworks to understand the ethical and moral implications related to patient safety, consent, autonomy, and data sharing.
Our goal is to provide a comprehensive overview of topics highlighting the opportunities and challenges of AI application to brain health and in particular to brain rehabilitation.
Healthcare and rehabilitation professionals are starting to team up with engineers, physicists, and computer scientists for Big Data use, to integrate AI-based sensing and assistive technologies into rehabilitation interventions and to develop effective and user-friendly human-computer interactions.
We encourage the submission of manuscripts emphasizing and highlighting an interdisciplinary approach.
We seek Original Research, Review, Mini-Review, Hypothesis and Theory, Perspective, Clinical Trial, Case Report, and Opinion articles covering, but not limited to, the following:
• Evidence supporting the applications of original research related to AI/Big Data for diagnostics related to brain health/rehabilitation
• Original research related to AI data visualization, sensing and assistive technologies for rehabilitation interventions
• Challenges of incorporating individual variability and client-centered care within the AI framework.
• Changing/future roles of healthcare and rehabilitation professionals
• Ethical challenges of AI on patient privacy, autonomy, data security, data management
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.
