Machine Learning for Health: Algorithm Auditing & Quality Control.

Oala, Luis, Murchison, Andrew G, Balachandran, Pradeep, Choudhary, Shruti, Fehr, Jana, Leite, Alixandro Werneck, Goldschmidt, Peter G, Johner, Christian, Schörverth, Elora D M, Nakasi, Rose, Meyer, Martin, Cabitza, Federico, Baird, Pat, Prabhu, Carolin, Weicken, Eva, Liu, Xiaoxuan, Wenzel, Markus, Vogler, Steffen, Akogo, Darlington, Alsalamah, Shada, Kazim, Emre, Koshiyama, Adriano, Piechottka, Sven, Macpherson, Sheena, Shadforth, Ian, Geierhofer, Regina, Matek, Christian, Krois, Joachim, Sanguinetti, Bruno, Arentz, Matthew, Bielik, Pavol, Calderon-Ramirez, Saul, Abbood, Auss, Langer, Nicolas, Haufe, Stefan, Kherif, Ferath, Pujari, Sameer, Samek, Wojciech and Wiegand, Thomas (2021) Machine Learning for Health: Algorithm Auditing & Quality Control. Journal of medical systems, 45 (12). p. 105. ISSN 1573-689X. This article is available to all UHB staff and students login using UHB Athens Account. Register for Athens here - https://openathens.nice.org.uk/

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Official URL: https://link.springer.com/article/10.1007%2Fs10916...

Abstract

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Item Type: Article
Additional Information: This article is available to all UHB staff and students login using UHB Athens Account. Register for Athens here - https://openathens.nice.org.uk/
Subjects: QA Mathematics. Computing
Divisions: Ambulatory Care > Ophthalmology
Related URLs:
Depositing User: Ms Willow Baylis
Date Deposited: 09 Nov 2021 09:50
Last Modified: 09 Nov 2021 09:50
URI: http://www.repository.uhblibrary.co.uk/id/eprint/4781

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