Smartphone AI help for reading heart tracings in emergency care
Tests a smartphone AI tool for spotting heart attacks on ECGs by emergency providers.
Plain-language title generated from the registry record
Registry title: Smartphone AI Assistance for Prehospital ECG Interpretation
Key facts
- Where
- Murten/Morat, SwitzerlandSee the site
Where in Switzerland
- Caserne des pompiers de Morat (Swiss French-speaking prehospital clinical research conference)Murten/Morat, SwitzerlandTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- École Supérieure de Soins Ambulanciers - College of Higher Education in Prehospital Care
- Trial code
- NCT07810686
- Record updated
- 9 September 2026
- Original description
Prehospital providers interpret 12-lead electrocardiograms (ECGs) under time pressure and without immediate expert support. Missed acute coronary occlusion - occlusion myocardial infarction (OMI) - delays reperfusion, while false positive interpretations trigger unnecessary catheterization laboratory activations. Multimodal large language models (LLMs) available on any smartphone can now analyze a photographed ECG, and prehospital providers have begun using them spontaneously. No randomized trial has evaluated whether this practice improves diagnostic performance.Read full original descriptionShow less
AI & Prehospital ECG Analysis: A Randomized Controlled Trial of a Smartphone Large Language Model for Occlusion Myocardial Infarction Detection by Prehospital Providers
Prehospital providers interpret 12-lead electrocardiograms (ECGs) under time pressure and without immediate expert support. Missed acute coronary occlusion - occlusion myocardial infarction (OMI) - delays reperfusion, while false positive interpretations trigger unnecessary catheterization laboratory activations. Multimodal large language models (LLMs) available on any smartphone can now analyze a photographed ECG, and prehospital providers have begun using them spontaneously. No randomized trial has evaluated whether this practice improves diagnostic performance.
This randomized controlled trial compares the diagnostic performance of prehospital providers interpreting ECG clinical vignettes with and without mandatory assistance from a single, version-locked smartphone large language model. Participants - paramedics, emergency medical technicians, nurses and physicians practicing in prehospital care in French-speaking Switzerland - are randomized 1:1 on a dedicated digital platform and answer 14 clinical vignettes presented in individually randomized order. Each vignette is built around a real, anonymized 12-lead ECG obtained during routine clinical care.
The primary outcome is the proportion of vignettes for which the participant correctly identifies the presence or absence of an OMI. Secondary outcomes are sensitivity, specificity, and the accuracy of the prehospital priority decision level.
Inclusion: Prehospital care provider practising in French-speaking Switzerland; Any level of training: emergency medical technician, paramedic (ES), nurse (ES/HES) in prehospital emergency care, or prehospital emergency physician; Electronic informed consent signed before randomization.
Exclusion: Cardiologist; Any person not practising in prehospital care; Insufficient command of written French to answer the vignettes reliably; Refusal to participate or withdrawal of consent.