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Even though robotic technology holds great potential for performing spinal surgery and advancing neurosurgical techniques, it is of utmost importance to establish its practicality and to demonstrate better clinical outcomes compared with traditional techniques, especially in the current cost-effective era. Several systems have proved to be safe and reliable in the execution of tasks on a routine basis, are commercially available, and are used for specific indications in spine surgery. However, workflow, usability, interdisciplinary setups, efficacy, and cost-effectiveness have to be proven prospectively. This article includes a short description of robotic structures and workflow, followed by preliminary results of a randomized prospective study comparing conventional free-hand techniques with routine spine navigation and robotic-assisted procedures. Additionally, we present cases performed with a spinal robotic device, assessing not only the accuracy of the robotic-assisted procedure but also other factors (eg, minimal invasiveness, radiation dosage, and learning curves). Currently, the use of robotics in spinal surgery greatly enhances the application of minimally invasive procedures by increasing accuracy and reducing radiation exposure for patients and surgeons compared with standard procedures. Second-generation hardware and software upgrades of existing devices will enhance workflow and intraoperative setup. As more studies are published in this field, robot-assisted therapies will gain wider acceptance in the near future.

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign. The authors review the advantages and future prospects of neuromorphic computing, a multidisciplinary engineering concept for energy-efficient artificial intelligence with brain-inspired functionality.

Energy-optimized hybrid computers with a range of processor accuracies will advance modelling in fields from climate change to neuroscience, says Tim Palmer.

A major architectural difference here is that the core components in an SoC-based microcomputer—e.g., the central processing unit (CPU)-housing microcontroller, memory blocks, voltage regulators, etc.—are built as a single, fully integrated circuit, enabling small chips with low power consumption, reduced heat dissipation, and so on. [...]many of these activities and projects have been done already (or at least started), so you can simply Google them and use other work or build on people's existing efforts (see also Tip 7). Because of its simple interface with sensors, motors, cameras, etc., the Pi can be used for many practical tasks (see again Fig 2). [...]Quora is a popular site, too, modelled on a rich question-and-answer functionality and offering its own Pi-based communities [33]. [...]as an illustration of its general utility and versatility, note that the Pi is also being adopted for instructional purposes in other areas, such as in radiology training programs [49].

Cybersecurity incidents are a growing threat to the health care industry in general and hospitals in particular. The health care industry has lagged behind other industries in protecting its main stakeholder (ie, patients), and now hospitals must invest considerable capital and effort in protecting their systems. However, this is easier said than done because hospitals are extraordinarily technology-saturated, complex organizations with high end point complexity, internal politics, and regulatory pressures. The purpose of this study was to develop a systematic and organizational perspective for studying (1) the dynamics of cybersecurity capability development at hospitals and (2) how these internal organizational dynamics interact to form a system of hospital cybersecurity in the United States. We conducted interviews with hospital chief information officers, chief information security officers, and health care cybersecurity experts; analyzed the interview data; and developed a system dynamics model that unravels the mechanisms by which hospitals build cybersecurity capabilities. We then use simulation analysis to examine how changes to variables within the model affect the likelihood of cyberattacks across both individual hospitals and a system of hospitals. We discuss several key mechanisms that hospitals use to reduce the likelihood of cybercriminal activity. The variable that most influences the risk of cyberattack in a hospital is end point complexity, followed by internal stakeholder alignment. Although resource availability is important in fueling efforts to close cybersecurity capability gaps, low levels of resources could be compensated for by setting a high target level of cybersecurity. To enhance cybersecurity capabilities at hospitals, the main focus of chief information officers and chief information security officers should be on reducing end point complexity and improving internal stakeholder alignment. These strategies can solve cybersecurity problems more effectively than blindly pursuing more resources. On a macro level, the cyber vulnerability of a country's hospital infrastructure is affected by the vulnerabilities of all individual hospitals. In this large system, reducing variation in resource availability makes the whole system less vulnerable-a few hospitals with low resources for cybersecurity threaten the entire infrastructure of health care. In other words, hospitals need to move forward together to make the industry less attractive to cybercriminals. Moreover, although compliance is essential, it does not equal security. Hospitals should set their target level of cybersecurity beyond the requirements of current regulations and policies. As of today, policies mostly address data privacy, not data security. Thus, policy makers need to introduce policies that not only raise the target level of cybersecurity capabilities but also reduce the variability in resource availability across the entire health care system.

Augmented reality (AR) superimposes computer-generated virtual objects onto the user’s view of the real world. Among medical disciplines, neurosurgery has long been at the forefront of image-guided surgery, and it continues to push the frontiers of AR technology in the operating room. In this systematic review, we explore the history of AR in neurosurgery and examine the literature on current neurosurgical applications of AR. Significant challenges to surgical AR exist, including compounded sources of registration error, impaired depth perception, visual and tactile temporal asynchrony, and operator inattentional blindness. Nevertheless, the ability to accurately display multiple three-dimensional datasets congruently over the area where they are most useful, coupled with future advances in imaging, registration, display technology, and robotic actuation, portend a promising role for AR in the neurosurgical operating room. Réalité augmentée en neurochirurgie : revue des concepts actuels et applications émergeantes. La réalité augmentée (RA) superpose des objets virtuels générés par ordinateur à la vision du monde réel de l’utilisateur. Parmi les disciplines médicales, la neurochirurgie a longtemps été à l’avant-garde de la chirurgie guidée par imagerie et continue de repousser les frontières de la technologie de RA en salle d’opération. Nous avons procédé à une revue systématique afin d’explorer l’histoire de la technologie de RA en neurochirurgie et nous avons examiné la littérature portant sur les applications neurochirurgicales actuelles de la RA. Il existe des défis importants dans ce domaine dont l’erreur d’alignement, la perception altérée de la profondeur, l’asynchronie temporelle visuelle et tactile et l’aveuglement due à l’inattention de l’opérateur. Néanmoins, la capacité de permettre une visualisation précise de multiples ensembles de données tridimensionnelles de façon congruente sur la zone où elles sont le plus utiles, couplée à des progrès qui seront réalisés en imagerie, en inscription, en technologie d’affichage et en actionnement robotique laisse entrevoir un rôle prometteur de la RA en salle d’opération neurochirurgicale.

Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge.

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

The study aims to develop and pilot a telehealth social emotional program, MindChip™ delivered with a computer based interventions (CBI) (Mind Reading © ) for autistic adults. MindChip™ combined four theoretical perspectives and community feedback underpinning the essential mechanisms for targeting the social emotional understanding of autistic adults. A randomised pragmatic pilot trial (N = 25) was conducted to explore the feasibility of MindChip™ (n = 11) and to understand the preliminary efficacy of combining it with CBI compared to CBI only (n = 14). The use of MindChip™ and CBI combined demonstrated partial feasibility, with preliminary efficacy findings revealing increased emotion recognition generalisation outcomes compared to CBI only. Further research is required to improve the engagement and personalisation of the intervention for autistic adults.