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; Wiegand, Thomas
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.
Published in: Journal of Medical Systems, 10.1007/s10916-021-01783-y, Springer Nature