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
Fraunhofer HHI, Berlin, Germany.
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
J Med Syst. 2021 Nov 2;45(12):105. doi: 10.1007/s10916-021-01783-y.
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.
提出新型健康领域机器学习（ML4H）工具的开发者常常承诺，其工具性能将达到甚至超越现有工具，但实际情况往往更为复杂。正如糖尿病视网膜病变或新冠病毒筛查的实例所示，将ML4H可靠地部署到现实世界具有挑战性。我们设想了一个算法审核与质量控制的集成框架，该框架为ML系统在医疗保健中的有效且可靠应用提供了一条途径。在这篇社论中，我们总结了为实现这一愿景而正在进行的工作，并呼吁参与本期刊的“健康领域机器学习：算法审核与质量控制”特刊，以推动ML4H审核实践的发展。
