BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel occlusions on CTA scans and notify the treatment team within minutes via a dedicated mobile application. We aimed to evaluate the detection accuracy of the Viz LVO in real clinical practice at a comprehensive stroke center. MATERIALS AND METHODS: Viz LVO was installed for this study in a comprehensive stroke center. All consecutive head and neck CTAs performed from January 2018 to March 2019 were scanned by the algorithm for detection of large-vessel occlusions. The system results were compared with the formal reports of senior neuroradiologists used as ground truth for the presence of a large-vessel occlusion. RESULTS: A total of 1167 CTAs were included in the study. Of these, 404 were stroke protocols. Seventy-five (6.4%) patients had a large-vessel occlusion as ground truth; 61 were detected by the system. Sensitivity was 0.81, negative predictive value was 0.99, and accuracy was 0.94. In the stroke protocol subgroup, 72 (17.8%) of 404 patients had a large-vessel occlusion, with 59 identified by the system, showing a sensitivity of 0.82, negative predictive value of 0.96, and accuracy of 0.89. CONCLUSIONS: Our experience evaluating Viz LVO shows that the system has the potential for early identification of patients with stroke with large-vessel occlusions, hopefully improving future management and stroke care.

This Viewpoint explains the “green pass” program in Israel to encourage residents to seek COVID-19 vaccination, with incentives including access to social, cult

Aim: This study explores the potential of a generative artificial intelligence tool (ChatGPT) as clinical support for nurses. Specifically, we aim to assess whether ChatGPT can demonstrate clinical decision‐making equivalent to that of expert nurses and novice nursing students. This will be evaluated by comparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience. Design: This is a cross‐sectional study. Methods: Emergency room registered nurses (i.e. experts; n = 30) and nursing students (i.e. novices; n = 38) were recruited during March–April 2023. Clinical decision‐making was measured using three validated clinical scenarios involving an initial assessment and reevaluation. Clinical decision‐making aspects assessed were the accuracy of initial assessments, the appropriateness of recommended tests and resource use and the capacity to reevaluate decisions. Performance was also compared by timing response generations and word counts. Expert nurses and novice students completed online questionnaires (via Qualtrics), while ChatGPT responses were obtained from OpenAI. Results: Concerning aspects of clinical decision‐making and compared to novices and experts: (1) ChatGPT exhibited indecisiveness in initial assessments; (2) ChatGPT tended to suggest unnecessary diagnostic tests; (3) When new information required re‐evaluation, ChatGPT responses demonstrated inaccurate understanding and inappropriate modifications. In terms of performance, the mean number of words utilized in ChatGPT answers was 27–41 times greater than that utilized by both experts and novices; and responses were provided approximately 4 times faster than those of novices and twice faster than expert nurses. ChatGPT responses maintained logical structure and clarity. Conclusions: A generative AI tool demonstrated indecisiveness and a tendency towards over‐triage compared to human clinicians. Impact: The study shows that it is important to approach the implementation of ChatGPT as a nurse's digital assistant with caution. More study is needed to optimize the model's training and algorithms to provide accurate healthcare support that aids clinical decision‐making. Reporting method: This study adhered to relevant EQUATOR guidelines for reporting observational studies. Patient or public contribution: Patients were not directly involved in the conduct of this study.

Background: Low socioeconomic status (SES) groups have been disproportionately affected by the COVID-19 pandemic. We aimed to examine COVID-19 vaccination rate by neighborhood SES and ethnicity in Israel, a country which has achieved high vaccination rates. Methods: Data on vaccinations were obtained from the Israeli Ministry of Health’s open COVID-19 database, for December 20, 2020 to August 31, 2021. Correlation between vaccination rate and neighborhood SES was analyzed. Difference in vaccination rate between the first and second vaccine dose was analyzed by neighborhood SES and ethnicity. Findings: A clear socioeconomic gradient was demonstrated, with higher vaccination rates in the higher SES categories (first dose: r = 0.66; second dose: r = 0.74; third dose: r = 0.92). Vaccination uptake was lower in the lower SES groups and in the Arab population, with the largest difference in uptake between Jewish and Arab localities for people younger than 60, and with the gap widening between first and third doses. Conclusions: Low SES groups and the Arab ethnic minority demonstrated disparities in vaccine uptake, which were greater for the second and third, compared with the first vaccine dose. Strategies to address vaccination inequity will need to identify barriers, provide targeted information, and include trust-building in disadvantaged communities.

More than 770 artificial intelligence medical devices cleared by the U.S. Food and Drug Administration use deep learning in radiology, yet comprehensive solutions integrating data through centralized platforms have not reached their potential.