Bojic, Suzana (55965837500)Suzana (55965837500)BojicRadovanovic, Nemanja (58926060900)Nemanja (58926060900)RadovanovicRadovic, Milica (58926273200)Milica (58926273200)RadovicStamenkovic, Dusica (23037217500)Dusica (23037217500)Stamenkovic2025-06-122025-06-122024https://doi.org/10.1515/sjpain-2023-0136https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187130006&doi=10.1515%2fsjpain-2023-0136&partnerID=40&md5=7f0a48ceb76e766d074d3b689b2b6729https://remedy.med.bg.ac.rs/handle/123456789/1430Background ‒ Generative artificial intelligence (AI) models offer potential assistance in pain research data acquisition, yet concerns persist regarding data accuracy and reliability. In a comparative study, we evaluated open generative AI models’ capacity to acquire data on acute pain in rock climbers comparable to field research. Methods ‒ Fifty-two rock climbers (33 m/19 f; age 29.0 [24.0–35.75] years) were asked to report pain location and intensity during a single climbing session. Five generative pretrained transformer models were tasked with responses to the same questions. Results ‒ Climbers identified the back of the forearm (19.2%) and toes (17.3%) as primary pain sites, with reported median pain intensity at 4 [3–5] and median maximum pain intensity at 7 [5–8]. Conversely, AI models yielded divergent findings, indicating fingers, hands, shoulders, legs, and feet as primary pain localizations with average and maximum pain intensity ranging from 3 to 4.4 and 5 to 10, respectively. Only two AI models provided references that were untraceable in PubMed and Google searches. Conclusion ‒ Our findings reveal that, currently, open generative AI models cannot match the quality of field-collected data on acute pain in rock climbers. Moreover, the models generated nonexistent references, raising concerns about their reliability. © 2024 the author(s), published by De Gruyter.acute painartificial intelligenceclimbinggenerative pretrained transformer