Key result
EMS providers view AI-based prehospital stroke tools as a supplementary aid requiring trust and feasibility.
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EMS providers view prehospital AI stroke diagnostic tools as supplementary aids that require cross-disciplinary trust and practical feasibility to be successfully implemented.
BACKGROUND: Stroke is a large and increasing health challenge, leading to acquired physical disability and mortality. A rapid diagnostic assessment in the acute phase of a stroke is crucial and highly time dependent. Studies suggest that artificial intelligence (AI) could contribute for prognostication, prediction and resource optimization in suspected stroke cases in prehospital emergency care. The objective of the current study was to explore Emergency Medical Services providers' perspectives on using AI in the prehospital assessment of patients with a suspected stroke diagnosis. METHODS: A qualitative study design following stroke case simulation with an AI-based diagnostic tool was used. One focus group and ten dyadic interviews were conducted comprising 24 participants from three ambulance stations in Norway and Sweden respectively. Data were analyzed following Braun and Clarke's steps for thematic analysis. RESULTS: Three themes were identified, namely (1) Another tool in the toolkit, (2) Trust is essential, and (3) The devil is in the details. The participants underlined that the AI-based tool was just an addition to their usual assessment, including symptoms, anamnesis, and vital parameters, as well as their own 'clinical eye'. Moreover, trust was needed from various stakeholders for the tool to have a function in the patient pathway. Finally, size and weight, as well as the ability to differentiate between hemorrhagic and thrombotic stroke were central aspects for the tool to be feasible. CONCLUSION: Emergency Medical Services providers mainly rely on their own clinical eye, combining symptoms, anamnesis and measurement of vital parameters when assessing suspected stroke patients. AI-based tools may be used as support in the decision-making process, however this depends on the establishment of trust in the tool across EMS providers, neurologists and other health professionals.
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Leonardsen et al. (2025) studied Suspected stroke (n=24). AI-based diagnostic tool for prehospital stroke assessment was evaluated on Emergency Medical Services providers' perspectives on using AI in prehospital assessment. Emergency Medical Services providers viewed AI-based diagnostic tools for prehospital stroke assessment as a supplementary aid requiring trust across stakeholders and practical feasibility.
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