Reply to the letter to the editor: “A critical evaluation on the use of large language model for radiology research”
Author:
Funder
Ministerio de Ciencia e Innovación
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-10333-8.pdf
Reference5 articles.
1. Martín-Noguerol T, Paulano-Godino F, Martín-Valdivia MT et al (2019) Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol 16:1239–1247. https://doi.org/10.1016/j.jacr.2019.05.047
2. López-Úbeda P, Martín-Noguerol T, Luna A (2023) Radiology, explicability and AI : closing the gap. Eur Radiol 8–10. https://doi.org/10.1007/s00330-023-09902-8
3. Elkassem AA, Smith AD (2023) Potential use cases for ChatGPT in radiology reporting. AJR Am J Roentgenol. https://doi.org/10.2214/ajr.23.29198
4. Shen Y, Heacock L, Elias J et al (2023) ChatGPT and other large language models are double-edged swords. Radiology 307(2):e230163. https://doi.org/10.1148/radiol.230163
5. Martín-Noguerol T, Paulano-Godino F, López-Ortega R et al (2021) Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team. Clin Radiol 76:317–324. https://doi.org/10.1016/j.crad.2020.11.113
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