@article{10.37349/ec.2026.1012114,
abstract = {Aim: Artificial intelligence may support syncope evaluation, but reliability of language models in structured syncope care remains uncertain. We evaluated diagnostic performance, safety, and within-case consistency of Generative Pre-trained Transformer-5 (GPT-5) in patients with transient loss of consciousness (T-LOC). Methods: This prospective cohort study included 55 patients evaluated in syncope units. GPT-5 and a syncope-expert assessed identical case information after core evaluation (CE: history-taking, physical examination, active standing test, and 12-lead electrocardiogram) and extended evaluation (EE: CE plus additional testing when indicated). An expert panel adjudicated the final diagnosis after 18 months. Outcomes were diagnostic yield, final-diagnosis inclusion rate, diagnostic precision score (DPS), cardiac diagnostic safety, and within-case consistency across five repeated GPT-5 runs. Results: Of 55 patients, 54 had complete follow-up for performance analyses. Diagnostic yield was 94% for the syncope-expert at CE and EE, and 100% and 96% for GPT-5 at CE and EE, respectively. GPT-5 included the final diagnosis in 52% (CE) and 57% (EE) of cases, versus 67% for the syncope-expert. DPS remained negative for GPT-5 at CE (mean −0.03, SD 0.54) and EE (mean −0.01, SD 0.49). Among four final cardiac syncope cases, GPT-5 selected the final diagnosis in one case and the syncope-expert in three. First-diagnosis consistency across five GPT-5 runs was 69% after CE and 74% after EE. Conclusions: GPT-5 generated diagnostic outputs frequently but showed limited precision, cardiac safety concerns, and within-case variability. Its role in syncope evaluation should remain supportive within clinician-led pathways rather than autonomous.},
author = {van Zanten, Steven and Boel, Thomas T. and de Jong, Jelle SY and Koomen, Egbert M and Bais, Babette and Dara, Ako and Giele, Freek and Geertsma, Christiaan and Sutton, Richard and Scheffer, Mike G and de Groot, Joris R and de Lange, Frederik J},
doi = {10.37349/ec.2026.1012114},
journal = {Exploration of Cardiology},
elocation-id = {1012114},
title = {Diagnostic performance of artificial intelligence in the syncope unit},
url = {https://www.explorationpub.com/Journals/ec/Article/1012114},
volume = {4},
year = {2026}
}