| Autor: |
Babak Salam, Dmitrij Kravchenko, Sebastian Nowak, Alois M. Sprinkart, Leonie Weinhold, Anna Odenthal, Narine Mesropyan, Leon M. Bischoff, Ulrike Attenberger, Daniel L. Kuetting, Julian A. Luetkens, Alexander Isaak |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Cardiovascular Magnetic Resonance, Vol 26, Iss 1, Pp 101035- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1097-6647 |
| DOI: |
10.1016/j.jocmr.2024.101035 |
| Popis: |
Background: Patients are increasingly using Generative Pre-trained Transformer 4 (GPT-4) to better understand their own radiology findings. Purpose: To evaluate the performance of GPT-4 in transforming cardiovascular magnetic resonance (CMR) reports into text that is comprehensible to medical laypersons. Methods: ChatGPT with GPT-4 architecture was used to generate three different explained versions of 20 various CMR reports (n = 60) using the same prompt: “Explain the radiology report in a language understandable to a medical layperson”. Two cardiovascular radiologists evaluated understandability, factual correctness, completeness of relevant findings, and lack of potential harm, while 13 medical laypersons evaluated the understandability of the original and the GPT-4 reports on a Likert scale (1 “strongly disagree”, 5 “strongly agree”). Readability was measured using the Automated Readability Index (ARI). Linear mixed-effects models (values given as median [interquartile range]) and intraclass correlation coefficient (ICC) were used for statistical analysis. Results: GPT-4 reports were generated on average in 52 s ± 13. GPT-4 reports achieved a lower ARI score (10 [9–12] vs 5 [4–6]; p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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