Radiology, explicability and AI: closing the gap
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-09902-8.pdf
Reference14 articles.
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2. López-Úbeda P, Martín-Noguerol T, Juluru K, Luna A (2022) Natural language processing in radiology: update on clinical applications. J Am Coll Radiol 19(11):1271–1285. https://doi.org/10.1016/j.jacr.2022.06.016
3. Kotter E, Ranschaert E (2021) Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow. Eur Radiol 31:5–7. https://doi.org/10.1007/s00330-020-07148-2
4. Gallix B, Chong J (2019) Artificial intelligence in radiology: who’s afraid of the big bad wolf? Eur Radiol 29:1637–1639
5. Herm LV, Heinrich K, Wanner J, Janiesch C (2022) Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. Int J Inf Manage. https://doi.org/10.1016/j.ijinfomgt.2022.102538
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