ArticleBMC emergency medicine2025
Emergency medical services providers' perspectives on the use of artificial intelligence in prehospital identification of stroke- a qualitative study in Norway and Sweden.
Article in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Health Economic & Implementation Determinants of Prehospital Stroke Triage Tools: A Systematic Review.Journal of evaluation in clinical practice · 2026Pooled it
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Review
Corrections and comments
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke 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.
methodsA 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.
resultsThree 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.
conclusionEmergency 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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