Making Machine Learning Models Clinically Useful.
By Nigam H. Shah, Arnold Milstein, Steven C Bagley PhD. 2019
250 citations recorded by the source corpus.
Subfields: General Health Professions, Health Informatics, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In General Health Professions
In Health Informatics
In Radiology, Nuclear Medicine and Imaging
- Gary S. Collins
- B. van Ginneken
- Fabian Isensee
- Klaus Hermann Maier-Hein
- Geert J. S. Litjens
- Michael Alexander Riegler
- Lauren Wilcox
- Tal Arbel
Citations in the corpus
- A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy (2020, background): “This is a problem because the success of a deep learning model does not rest solely on its accuracy, but also on its ability to improve patient care [35].”
- The shaky foundations of large language models and foundation models for electronic health records (2023, background): “Developing frameworks for determining a model ’ s overall worth remains indispensable 79 .”
- Metrics Reloaded: Recommendations for image analysis validation (2022, background): “In fact, validation that is not conducted according to relevant metrics could be one major reason for why many artificial intelligence (AI) developments in medical imaging fail to reach clinical practice 1,2 .”