This paper is in the following e-collection/theme issue:

Viewpoints on and Experiences with Digital Technologies in Health (68) Machine Learning (1401) Imaging Informatics (187)

Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28776, first published .
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

Authors of this article:

Viraj Kulkarni1 Author Orcid Image ;   Manish Gawali1 Author Orcid Image ;   Amit Kharat1, 2 Author Orcid Image
Article Authors Cited by (8) Tweetations (4) Metrics

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  2. Ghuwalewala S, Kulkarni V, Pant R, Kharat A. Levels of Autonomous Radiology. Interactive Journal of Medical Research 2022;11(2):e38655 View
  3. Furtado F, Catalano O. Editorial for “Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation”. Journal of Magnetic Resonance Imaging 2023;58(2):442 View
  4. Kulkarni V, Pawale S, Kharat A. A classical–quantum convolutional neural network for detecting pneumonia from chest radiographs. Neural Computing and Applications 2023;35(21):15503 View
  5. Brereton T, Malik M, Lifson M, Greenwood J, Peterson K, Overgaard S. The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review. Interactive Journal of Medical Research 2023;12:e45903 View
  6. Anand A, Krithivasan S, Roy K. RoMIA: a framework for creating Robust Medical Imaging AI models for chest radiographs. Frontiers in Radiology 2024;3 View
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  8. Balagopalan A, Baldini I, Celi L, Gichoya J, McCoy L, Naumann T, Shalit U, van der Schaar M, Wagstaff K, Badawi O. Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact. PLOS Digital Health 2024;3(4):e0000474 View