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Editorial
. 2021 Nov 2;45(12):105.
doi: 10.1007/s10916-021-01783-y.

Machine Learning for Health: Algorithm Auditing & Quality Control

Luis Oala  1 , Andrew G Murchison  2 , Pradeep Balachandran  3 , Shruti Choudhary  4 , Jana Fehr  5 , Alixandro Werneck Leite  6 , Peter G Goldschmidt  7 , Christian Johner  8 , Elora D M Schörverth  9 , Rose Nakasi  10 , Martin Meyer  11 , Federico Cabitza  12 , Pat Baird  13 , Carolin Prabhu  14 , Eva Weicken  9 , Xiaoxuan Liu  15 , Markus Wenzel  9 , Steffen Vogler  16 , Darlington Akogo  17 , Shada Alsalamah  18   19 , Emre Kazim  20 , Adriano Koshiyama  20 , Sven Piechottka  21 , Sheena Macpherson  22 , Ian Shadforth  22 , Regina Geierhofer  23 , Christian Matek  24 , Joachim Krois  25 , Bruno Sanguinetti  26 , Matthew Arentz  27 , Pavol Bielik  28 , Saul Calderon-Ramirez  29 , Auss Abbood  30 , Nicolas Langer  31 , Stefan Haufe  32 , Ferath Kherif  33 , Sameer Pujari  19 , Wojciech Samek  9 , Thomas Wiegand  9
Affiliations
Editorial

Machine Learning for Health: Algorithm Auditing & Quality Control

Luis Oala et al. J Med Syst. .

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.

Keywords: Algorithm; Artificial intelligence; Auditing; Health; Machine learning; Quality control.

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Figures

Fig. 1
Fig. 1
Process overview. A: Most ML tools share a set of core components comprising data, a ML-model and its outputs B: The typical ML life cycle goes through stages of planning, development, validation and, potentially, deployment under appropriate monitoring C: An ML4H audit is carried out with respect to a dynamic set of technical, clinical and regulatory considerations that depend on the concrete ML technology and the intended use of the tool
See this image and copyright information in PMC

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