Machine Learning for Health: Algorithm Auditing & Quality Control

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 scree...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of medical systems Vol. 45; no. 12; p. 105
Main Authors:	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, Wiegand, Thomas
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-12-2021 Springer Nature B.V
Subjects:	Algorithms Artificial intelligence COVID-19 Diabetes mellitus Diabetic retinopathy Health care Health Informatics Health Sciences Humans Implementation Science & Operations Management Learning algorithms Machine Learning Machine Learning for Health: Algorithm Auditing & Quality Control Medicine Medicine & Public Health Quality Control Retinopathy Statistics for Life Sciences System effectiveness Health Artificial intelligence Algorithm Machine learning Quality control Auditing
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
Bibliography:	SourceType-Scholarly Journals-1 content type line 23 ObjectType-Editorial-2 ObjectType-Commentary-1
ISSN:	0148-5598 1573-689X 1573-689X
DOI:	10.1007/s10916-021-01783-y