International evaluation of an AI system for breast cancer screening

By Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, H. Ashrafian, Trevor Back, Mary Chesus, Greg C. Corrado, Ara Darzi and 21 more. 2020

2,721 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “…the gold standard is to evaluate models on an independent test set collected from a different hospital (e.g.,Beck et al. (2011); Liu et al. (2017); Courtiol et al. (2019); McKinney et al. (2020)) or at least with a different scanner within the same hospital (e.g., Campanella et al. (2019)).”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, neutral): “Ensembles of pre-trained models can also be ﬁne-tuned to achieve strong performance as demonstrated in [121].”
Learning medical triage from clinicians using Deep Q-Learning (2020, background): “At the turn of the last decade, the field of automated clinical decision-making was invigorated by the revolution in deep-learning approaches with several applications in perceptual settings, in which the decision relies on image recognition and not on clinical signs [10, 11].”
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making (2021, background): “1 INTRODUCTION As AI performance grows rapidly and often surpasses humans in constrained tasks [4, 23, 27, 48, 57], a critical challenge to enable social good is to understand how AI assistance can be used to enhance human performance.”
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “A notable study published in the journal Nature reported the development of an AI model by Google Health [35].”
Deep learning in histopathology: the path to the clinic (2021, background): “Other sources of variation have not yet been taken into account in CPATH, such as differing patient populations between centers or countries, although they are starting to be considered in other fields such as radiology 116 .”
Big Self-Supervised Models Advance Medical Image Classification (2021, background): “Despite the differences in image statistics, scale, and taskrelevant features, transfer learning from natural images is commonly used in medical image analysis [28, 30, 31, 45], and multiple empirical studies show that this improves performance [1, 15, 20].”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, neutral): “18).”
Towards Generalist Biomedical AI (2023, background): “Consider an existing AI system for interpreting mammograms [4].”
Surgical data science – from concepts toward clinical translation (2020, background): “Although recent efforts have been made in healthcare (McKinney et al., 2020) to include test data from different clinical sites, these still remain limited.”
Revolutionizing healthcare: the role of artificial intelligence in clinical practice (2023, methodology): “This study showed that utilizing an AI system to interpret mammograms had an absolute reduction in false positives and false negatives by 5.7% and 9.4%, respectively [11].”