Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers-From the Radiology Editorial Board.

By David A. Bluemke, Linda Moy, M. Bredella, B. Ertl-Wagner, Kathryn J Fowler, V. Goh, E. Halpern, C. P. Hess, M. Schiebler, C. Weiss. Radiology, 2019

318 citations recorded by the source corpus.

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/969c2adbea6ee009fd3d102479cc71e6013ecf79

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Y. Matsui
2. A. Yamada
3. K. Hirata
4. N. Fujima
5. Y. Fushimi
6. S. Naganawa
7. S. Fujita
8. K. Kamagata

Citations in the corpus

• Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “This common scenario means that estimations of AI performance based on internal test data may overestimate its real‑world performance on external data [73–75].”