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Medical education faces a difficult challenge today; an exponential increase in knowledge and the rise and rise of disruptive technologies are making traditional education obsolete. As the world nears the era of Industry and Healthcare 4.0, the medical community needs to keep up and prepare physicians for a hyper-connected digital world. Virtual neurological care is poised to be at the forefront of care delivery claims, yet the virtual communication of neurological knowledge is still in its infancy. This increasing digitalization of care and education is both an opportunity and a challenge. With this paper, the authors aim to bridge the gap between technology and neurological education. After a thorough review of recent literature and assessing current trends, the authors propose that contemporary medical education must adhere to the following tenets: Hybrid, Mobile, Mixed-reality, Open Access, Collaborative, Peer-reviewed, Intelligent, Game-based, and Global. We identify and align education objectives with the needs of future digital neurologists. The authors also discuss real-world advances that are aligned to serve the next generation of patients and providers., Competing Interests: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Junaid Kalia is employed at VeeOne Health Inc. Zaitoon Zafar, Muhammad Umair, Filzah Faheem, Danish Bhatti, Junaid Kalia are all participating authors in Neurologypocketbook.com. Danish Bhatti is the course director for "Mini-Fellowship in Movement Disorders'' at the University of Nebraska and "Mini-Fellowship in stroke and vascular neurology" at Medical Education, Research, International training and transfer of Technology (MERIT) of Association of Physicians of Pakistani Descent of North America (APPNA)., (Copyright © 2022, Zafar et al.)

Artificial intelligence is taking the world by storm and soon will be aiding patients in their journey at the hospital. The trials and tribulations of the healthcare system during the COVID-19 pandemic have set the stage for shifting healthcare from a physical to a cyber-physical space. A physician can now remotely monitor a patient, admitting them only if they meet certain thresholds, thereby reducing the total number of admissions at the hospital. Coordination, communication, and resource management have been core issues for any industry. However, it is most accurate in healthcare. Both systems and providers are exhausted under the burden of increasing data and complexity of care delivery, increasing costs, and financial burden. Simultaneously, there is a digital transformation of healthcare in the making. This transformation provides an opportunity to create systems of care that are artificial intelligence-enabled. Healthcare resources can be utilized more justly. The wastage of financial and intellectual resources in an overcrowded healthcare system can be avoided by implementing IoT, telehealth, and AI/ML-based algorithms. It is imperative to consider the design principles of the patient's journey while simultaneously prioritizing a better user experience to alleviate physician concerns. This paper discusses the entire blueprint of the AI/ML-assisted patient journey and its impact on healthcare provision., Competing Interests: Authors TN, MM and MA were employed by company NeuroCare.AI Neuroscience Academy. Author JK was employed by company NeuroCare.AI and Neurologypocketbook.com., (Copyright © 2022 Nazir, Mushhood Ur Rehman, Asghar and Kalia.)

Virtual care is here to stay. The explosive expansion of telehealth caused by the SARS-CoV-2 pandemic is more than a necessary measure of protection. The key drivers of this transition in healthcare delivery to a virtual setting are changes in patient behavior and expectations and societal attitudes, and prevailing technologies that are impossible to ignore. The younger population - Generation Z - is increasingly connected and mobile-first. We are heading to a world where we expect to see healthcare in general and neurology, in particular, delivered virtually. The medical community should prepare for this overhaul; proper implementation of virtual care from the ground up is the need of the hour. In an era of virtualization, it is up to the medical community to ensure a well-informed patient population, overcome cultural differences and build digital infrastructure with enhanced access and equity in care delivery, especially for the aging neurological patient population, which is not technologically savvy. Virtual care is a continuum of care that needs deeper integration at systematic levels. The design principles of a patient's journey need to be incorporated while simultaneously placing physician satisfaction with a better user experience at the center of implementation. In this paper, we discuss common challenges and pitfalls of virtual care implementation in neurology - logistical, technical, medicolegal, and those faced in incorporating health and medical education into virtual care - intending to provide solutions and strategies., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs., Competing Interests: JK is employed by VeeOne Health. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Marasini, Shrestha, Phuyal, Zaidat and Kalia.)

Teleneurology has provided access to neurological expertise and state-of-the-art stroke care where previously they have been inaccessible. The use of Artificial Intelligence with machine learning to assist telestroke care can be revolutionary. This includes more rapid and more reliable diagnosis through imaging analysis as well as prediction of hospital course and 3-month prognosis. Intelligent Electronic Medical Records can search free text and provide decision assistance by analyzing patient charts. Speech recognition has advanced enough to be reliable and highly convenient. Smart contextually aware communication and alert programs can enhance efficiency of patient flow and improve outcomes. Automated data collection and analysis can make quality improvement and research projects quicker and much less burdensome. Despite current challenges, these synergistic technologies hold immense promise in enhancing the clinician experience, helping to reduce physician burnout while improving patient health outcomes at a lower cost. This brief overview discusses the multifaceted potential of AI use in telestroke., (Copyright © 2020 Ali, Hamid, Zaidat, Bhatti and Kalia.)

Idiopathic inflammatory myopathies (IIM) are a group of chronic, autoimmune conditions affecting primarily the proximal muscles. The most common types are dermatomyositis (DM), polymyositis (PM), necrotizing autoimmune myopathy (NAM), and sporadic inclusion body myositis (sIBM). Patients typically present with sub-acute to chronic onset of proximal weakness manifested by difficulty with rising from a chair, climbing stairs, lifting objects, and combing hair. They are uniquely identified by their clinical presentation consisting of muscular and extramuscular manifestations. Laboratory investigations, including increased serum creatine kinase (CK) and myositis specific antibodies (MSA) may help in differentiating clinical phenotype and to confirm the diagnosis. However, muscle biopsy remains the gold standard for diagnosis. These disorders are potentially treatable with proper diagnosis and initiation of therapy. Goals of treatment are to eliminate inflammation, restore muscle performance, reduce morbidity, and improve quality of life. This review aims to provide a basic diagnostic approach to patients with suspected IIM, summarize current therapeutic strategies, and provide an insight into future prospective therapies.

Background and Purpose: Intra-arterial reperfusion therapies are expanding frontiers in acute ischemic stroke (AIS) management but there is considerable variability in clinical practice. The use of general anesthesia (GA) is one example. We aimed to better understand sedation practices in AIS., Methods: An online survey was distributed to the 68 active members of the Society of Vascular and Interventional Neurology (SVIN). Survey development was based on discussions at the SVIN Endovascular Stroke Round Table Meeting (Chicago, IL, 2008). The final survey contained 12 questions. Questions were developed as single and multiple-item responses; with an option for a free-text response., Results: There was a 72% survey response rate (N = 49/68). Respondents were interventional neurologists in practice 1-5 years (71.4%, N = 35). The mean (±SD) AIS interventions performed per year at the respondents' institutions was 42.5 ± 25, median 35.0 (IQR 20, 60). The most frequent anesthesia type used was GA (anesthesia team), then conscious sedation (nurse administered), monitored anesthesia care (anesthesia team), and finally local analgesia alone. There was a preference for GA because of eliminating movement (65.3% of respondents; N = 32/49), perceived procedural safety (59.2%, N = 29/49), and improved procedural efficacy (42.9%, N = 21/49). However, cited limitations to GA included risk of time delay (69.4%, N = 34), of propagating cerebral ischemia due to hypoperfusion or other complications (28.6%, N = 14), and lack of adequate anesthesia workforce (20.4%, N = 7)., Conclusions: The most frequent type of anesthesia used by Neurointerventionalists for AIS interventions is GA. Prior to making GA standard of care during AIS intervention, more data are needed about effects on clinical outcomes.

Summary: This technical report describes the successful use of the newly introduced Distal Access Catheter, initially designed to work with the Merci Retrieval System with the Penumbra aspiration system as the main aspiration catheter. Both devices, one a clot retriever and the other a thrombo-aspiration device, can be used and deployed via the same catheter saving time during acute stoke intervention. Moreover, the larger inner diameter of the distal access catheter may allow more effective clot aspiration.