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In Journal of medical systems ; h5-index 48.0

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.

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


Algorithm, Artificial intelligence, Auditing, Health, Machine learning, Quality control