Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment

Julian Acosta; Scott Adams; Julius Kernbach; Romain Hardy; Sung Eun Kim; Luyang Luo; Xiaoman Zhang; Shreya Johri; Mohammed Baharoon; Pranav Rajpurkar

arXiv · 2507.22898

Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment

Julian Acosta·Scott Adams·Julius Kernbach·Romain Hardy·Sung Eun Kim·Luyang Luo·Xiaoman Zhang·Shreya Johri·Mohammed Baharoon·Pranav Rajpurkar

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Abstract

We developed a voice-driven artificial intelligence (AI) system that guides anyone - from paramedics to family members - through expert-level stroke evaluations using natural conversation, while also enabling smartphone video capture of key examination components for documentation and potential expert review. This addresses a critical gap in emergency care: current stroke recognition by first responders is inconsistent and often inaccurate, with sensitivity for stroke detection as low as 58%, causing life-threatening delays in treatment. Three non-medical volunteers used our AI system to assess ten simulated stroke patients, including cases with likely large vessel occlusion (LVO) strokes and stroke-like conditions, while we measured diagnostic accuracy, completion times, user confidence, and expert physician review of the AI-generated reports. The AI system correctly identified 84% of individual stroke signs and detected 75% of likely LVOs, completing evaluations in just over 6 minutes. Users reported high confidence (median 4.5/5) and ease of use (mean 4.67/5). The system successfully identified 86% of actual strokes but also incorrectly flagged 2 of 3 non-stroke cases as strokes. When an expert physician reviewed the AI reports with videos, they identified the correct diagnosis in 100% of cases, but felt confident enough to make preliminary treatment decisions in only 40% of cases due to observed AI errors including incorrect scoring and false information. While the current system's limitations necessitate human oversight, ongoing rapid advancements in speech-to-speech AI models suggest that future versions are poised to enable highly accurate assessments. Achieving human-level voice interaction could transform emergency medical care, putting expert-informed assessment capabilities in everyone's hands.

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Julian Acosta, Scott Adams, Julius Kernbach, Romain Hardy, Sung Eun Kim, Luyang Luo, Xiaoman Zhang, Shreya Johri, Mohammed Baharoon, Pranav Rajpurkar. 2025-06-25. Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment. https://arxiv.org/abs/2507.22898

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