Smartphone AI Assistance for Prehospital ECG Interpretation

Condition(s) studied

Investigational drug(s) / intervention(s)

Smartphone large language model assistance (GPT-4o, version-locked): The platform transmits the ECG image to a single large language model (OpenAI GPT-4o, API snapshot gpt-4o-2024-08-06), locked for the entire study, together with a standardised prompt identical for all participants and all vignettes: "I am on an urgent prehospital call with a patient who presents this ECG. Analyse it and tell me what you think." Participants cannot modify the prompt, ask follow-up questions or provide additional clinical context. The model version and system fingerprint returned by the API are recorded for every call. The model's interpretation is displayed within the vignette. Use of the tool is mandatory; adherence to its interpretation is not.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Proportion of vignettes with correct identification of occlusion myocardial infarction (OMI) status — Single study session, approximately 90 minutes; 14 vignettes per participant
  For each of the 14 vignettes, the participant answers a binary question: "At this stage of care, is an OMI (acute coronary occlusion) likely? Yes / No". Responses are scored against a reference standard defined a priori, vignette by vignette, by the study cardiologist and locked before data collection. This reference standard is the answer expected of a prehospital provider at the point of care, anchored on coronary angiography wherever angiography is discriminant. For non-ischaemic mimics the expected answer depends on whether the acute presentation allows the condition to be distinguished from a coronary occlusion: it does not for the Takotsubo case (expected answer "yes"), whereas acute pericarditis is usually recognisable (expected answer "no"). This pre-specified departure from a purely angiographic standard is reported as such, and the analysis is repeated in a sensitivity analysis classifying all mimics as non-OMI. The outcome is the proportion of correctly classified vignettes

Trial sites (1)

Other trials for the same condition