Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The Utilization of ChatGPT in Reshaping Future Medical Education and Learning Perspectives: A Curse or a Blessing?
27
Zitationen
7
Autoren
2023
Jahr
Abstract
Background ChatGPT has substantial potential to revolutionize medical education. We aim to assess how medical students and laypeople evaluate information produced by ChatGPT compared to an evidence-based resource on the diagnosis and management of 5 common surgical conditions. Methods A 60-question anonymous online survey was distributed to third- and fourth-year U.S. medical students and laypeople to evaluate articles produced by ChatGPT and an evidence-based source on clarity, relevance, reliability, validity, organization, and comprehensiveness. Participants received 2 blinded articles, 1 from each source, for each surgical condition. Paired-sample t-tests were used to compare ratings between the 2 sources. Results Of 56 survey participants, 50.9% (n = 28) were U.S. medical students and 49.1% (n = 27) were from the general population. Medical students reported that ChatGPT articles displayed significantly more clarity (appendicitis: 4.39 vs 3.89, P = .020; diverticulitis: 4.54 vs 3.68, P < .001; SBO 4.43 vs 3.79, P = .003; GI bleed: 4.36 vs 3.93, P = .020) and better organization (diverticulitis: 4.36 vs 3.68, P = .021; SBO: 4.39 vs 3.82, P = .033) than the evidence-based source. However, for all 5 conditions, medical students found evidence-based passages to be more comprehensive than ChatGPT articles (cholecystitis: 4.04 vs 3.36, P = .009; appendicitis: 4.07 vs 3.36, P = .015; diverticulitis: 4.07 vs 3.36, P = .015; small bowel obstruction: 4.11 vs 3.54, P = .030; upper GI bleed: 4.11 vs 3.29, P = .003). Conclusion Medical students perceived ChatGPT articles to be clearer and better organized than evidence-based sources on the pathogenesis, diagnosis, and management of 5 common surgical pathologies. However, evidence-based articles were rated as significantly more comprehensive.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.774 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.685 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.244 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.