Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Readability analysis of ChatGPT's responses on lung cancer
31
Zitationen
1
Autoren
2024
Jahr
Abstract
were reviewed, and questions on the definition, etiology, risk factors, diagnosis, treatment, and prognosis of lung cancer (both NSCLC and SCLC) were selected. A set of 80 questions were asked 10 times to ChatGPT via the OpenAI API. ChatGPT's responses were tested using various readability formulas. The mean Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning FOG Scale, SMOG Index, Automated Readability Index, Coleman-Liau Index, Linsear Write Formula, Dale-Chall Readability Score, and Spache Readability Formula scores are at a moderate level (mean and standard deviation: 40.52 ± 9.81, 12.56 ± 1.66, 13.63 ± 1.54, 14.61 ± 1.45, 15.04 ± 1.97, 14.24 ± 1.90, 11.96 ± 2.55, 10.03 ± 0.63 and 5.93 ± 0.50, respectively). The readability levels of the answers generated by ChatGPT are "collage" and above and are difficult to read. Perhaps in the near future, the ChatGPT can be programmed to produce responses that are appropriate for people of different educational and age groups.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.626 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.532 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.046 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.843 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.