The neurologist · 2026
Artificial Intelligence in the Prehospital Stage of Stroke: Can Virtual Assistants Optimize Triage?
СтатьяНазвание и аннотация: Перевод в очереди
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Alessandro Lucas, Crema Santiago, Bianciotti Nicolás, Lombán Josefina, Perez Arana Carolina Irma, Bordón Orsingher Franco G, Kañevsky Agostina L, Pujol-Lereis Virginia, Fernandez Slezak Diego, Farez Mauricio F
Аннотация
OBJECTIVES: Many patients fail to access emergency services in time for acute stroke treatment. Artificial intelligence (AI) may help optimize prehospital triage. This study describes the development, refinement, and clinical validation of an AI-based virtual assistant (VA) for early stroke detection and appropriate emergency referral.
METHODS: A prospective cohort study was conducted between August 2024 and July 2025 in a tertiary care center in Buenos Aires, Argentina. The VA was applied to adult inpatients with acute stroke in a neurovascular unit. Before this, the tool had been optimized using a literature review and simulations with 1151 de-identified medical records. Clinical, demographic, and performance variables were recorded. The main outcomes were syndromic diagnostic alignment, identification of the most probable diagnosis, appropriate emergency referral, and user satisfaction.
RESULTS: A total of 78 participants were included (median age: 73 y; 56.4% male). The mean time from symptom onset to VA use was 2 days. Final diagnoses were ischemic stroke (80.8%), transient ischemic attack (11.5%), subarachnoid hemorrhage (5.1%), and intracerebral hemorrhage (2.6%). Syndromic diagnosis matched the clinical standard in 89.7% of cases; top-1 match in 71.8%, and top-3 in 91%. Emergency referral was adequate in 93.6% of cases. The median use involved 10 questions and 4 minutes. Over 90% rated the experience 4 or 5 out of 5.
CONCLUSIONS: In this controlled validation involving patients with confirmed cerebrovascular disease, the AI-based VA demonstrated high agreement with clinical syndromic classification, appropriate urgency recommendations, and high user acceptance. Further evaluation in broader prehospital populations is warranted.
METHODS: A prospective cohort study was conducted between August 2024 and July 2025 in a tertiary care center in Buenos Aires, Argentina. The VA was applied to adult inpatients with acute stroke in a neurovascular unit. Before this, the tool had been optimized using a literature review and simulations with 1151 de-identified medical records. Clinical, demographic, and performance variables were recorded. The main outcomes were syndromic diagnostic alignment, identification of the most probable diagnosis, appropriate emergency referral, and user satisfaction.
RESULTS: A total of 78 participants were included (median age: 73 y; 56.4% male). The mean time from symptom onset to VA use was 2 days. Final diagnoses were ischemic stroke (80.8%), transient ischemic attack (11.5%), subarachnoid hemorrhage (5.1%), and intracerebral hemorrhage (2.6%). Syndromic diagnosis matched the clinical standard in 89.7% of cases; top-1 match in 71.8%, and top-3 in 91%. Emergency referral was adequate in 93.6% of cases. The median use involved 10 questions and 4 minutes. Over 90% rated the experience 4 or 5 out of 5.
CONCLUSIONS: In this controlled validation involving patients with confirmed cerebrovascular disease, the AI-based VA demonstrated high agreement with clinical syndromic classification, appropriate urgency recommendations, and high user acceptance. Further evaluation in broader prehospital populations is warranted.