JMIR Neurotechnology

Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with the burden of chronic neurodegenerative diseases having doubled over the last 2 decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence (AI) and bioinformatic methods is changing this landscape. This paper explores this transformative journey, emphasizing the critical role of AI in neurology, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, integrating biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. In addition, telemedicine and web-based interactive health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions such as multiple sclerosis, Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease, and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of IT and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this paper, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in AI will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric health care.

Developing new clinical measures for degenerative cervical myelopathy (DCM) is an AO Spine RECODE-DCM research priority. Difficulties detecting DCM, and changes in DCM, cause diagnostic and treatment delays in clinical settings and heightened costs in clinical trials due to elevated recruitment targets. Digital outcome measures can tackle these challenges due to their ability to measure disease remotely, repeatedly, and more economically.

As a novel technology frontier, neurotechnology is revolutionizing our perceptions of the brain and nervous system. With growing private and public investments, a thriving ecosystem of direct-to-consumer neurotechnologies has also emerged. These technologies are increasingly being introduced in many parts of the world, including Africa. However, as the use of this technology expands, neuroethics and ethics of emerging technology scholars are bringing attention to the critical concerns it raises. These concerns are largely not new but are uniquely amplified by the novelty of technology. They include ethical and legal issues such as privacy, human rights, human identity, bias, autonomy, and safety, which are part of the artificial intelligence ethics discourse. Most importantly, there is an obvious lack of regulatory oversight and a dearth of literature on the consideration of contextual ethical principles in the design and application of neurotechnology in Africa. This paper highlights lessons African stakeholders need to learn from the ethics and governance of artificial intelligence to ensure the design of ethically responsible and socially acceptable neurotechnology in and for Africa.

Low sleep quality is a common symptom of multiple sclerosis (MS) and substantially decreases patients’ quality of life. The autonomic nervous system (ANS) is crucial to healthy sleep, and the transition from wake to sleep produces the largest shift in autonomic activity we experience every day. For patients with MS, the ANS is often impaired. The relationship between the ANS and perceived sleep quality in patients with MS remains elusive.

Recreational and leisure activities significantly contribute to the well-being of older adults, positively impacting physical, cognitive, and mental health. However, limited mobility and cognitive decline often impede access to these activities, particularly for individuals living with dementia. With the increasing availability of digital technologies, there is a rising interest in using technology to deliver recreation and leisure activities for cognitively impaired individuals, acknowledging its potential to provide diverse experiences. The COVID-19 pandemic further highlighted the need for virtual program delivery, especially for individuals in long-term care settings, leading to the development of tools like the Dementia Isolation Toolkit aimed at supporting compassionate isolation. To better support future implementations of the DIT, our rapid scoping review explores evidence-based, technology-enabled recreation programs for older adults with cognitive impairments, which promote well-being.

Invasive brain-computer interfaces (BCIs) are gaining attention for their transformative potential in human-machine interaction. These devices, which connect directly to the brain, could revolutionize medical therapies and augmentative technologies. This viewpoint examines recent advancements, weighs benefits against risks, and explores ethical and regulatory considerations for the future of invasive BCIs.

Quantitative pupillometry is used in mild traumatic brain injury (mTBI) with changes in pupil reactivity noted after blast injury, chronic mTBI, and sports-related concussion.

Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear.

The COVID-19 pandemic transformed neurological care by both requiring digital health modalities to reach patients and profoundly lowering barriers to digital health adoption. This combination of factors has given rise to a distinctive, emerging care model in neurology characterized by new technologies, care arrangements, and uncertainties. As the pandemic transitions to an endemic, there is a need to characterize the current and future states of this unique period in neurology.

Acquired brain injury (ABI) is a prominent cause of disability globally, with virtual reality (VR) emerging as a promising aid in neurorehabilitation. Nonetheless, the diversity among VR interventions can result in inconsistent outcomes and pose challenges in determining efficacy. Recent reviews offer best practice recommendations for designing and implementing therapeutic VR interventions to evaluate the acceptance of fully immersive VR interventions.

Implementing automated seizure detection in long-term electroencephalography (EEG) analysis enables the remote monitoring of patients with epilepsy, thereby improving their quality of life.

Recording time in invasive neuroscientific research is limited and must be used as efficiently as possible. Time is often lost due to a long setup time and errors by the researcher, driven by the number of manually performed steps. Currently, recording solutions that automate experimental overhead are either custom-made by researchers or provided as a submodule in comprehensive neuroscientific toolboxes, and there are no platforms focused explicitly on recording.