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GPT-4 as a Clinical Decision Support Tool in Ischemic Stroke Management: Evaluation Study
Additionally, in assessments modeled after neurology board exam questions, GPT-4 has shown a high accuracy rate, improving with repeated attempts [9,11,12].
The management of acute ischemic stroke (AIS) presents a critical and time-sensitive challenge in clinical settings. The approach to diagnosing and treating AIS requires a synthesis of information including patient symptoms, physical and neurological examinations, medical history, and imaging results.
JMIR AI 2025;4:e60391
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We then collaborated with nurse clinicians in the ED, cardiology, and neurology units to identify potential participants. We also displayed a poster in these nursing units to assist nurses and patients to understand the study. The research team requested the bedside nurse to gauge patient interest. If patient was interested, the research team approached them at the bedside. The research team member introduced the study, obtained informed written consent, and then began collecting data.
JMIR Form Res 2025;9:e60465
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In this study, we use business process modeling notation (BPMN) to map data flows and data repositories within neurology services at a regional neurosciences center. BPMN is a visual modeling language, primarily used in business analysis, which follows a standardized set of symbols and grammar for depicting workflows. There are two stages to BPMN: the creation of a visual workflow and the execution of that workflow as an automated process.
JMIR Med Inform 2024;12:e60017
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The health care professionals were physicians or psychologists who specialized in neurology or neuropsychology, registered nurses, practical care nurses, and physiotherapists or speech therapists.
Identified opportunities for DHIs in CVD were organized according to the clients, health care professionals, and data services, and they were classified into 14 main categories, 27 generic categories, and 126 subcategories.
J Med Internet Res 2024;26:e52715
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It is important to acknowledge that while this paper aims to provide a comprehensive overview of the impact of AI on neurology, its scope is necessarily limited. We have focused on key areas that, in our assessment, have most significantly influenced the field of neurology over the past quarter-century. The subsequent 5 chapters of this paper dive deeper into these advancements, exploring how they are reshaping the landscape of neurological care and research (Figures 1 and 2).
JMIR Neurotech 2024;3:e59556
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