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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.
  • Oala L; Fraunhofer HHI, Berlin, Germany. luis.oala@hhi.fraunhofer.de.
  • Murchison AG; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
  • Balachandran P; Technical Consultant (Digital Health), Thiruvananthapuram, India.
  • Choudhary S; University of Oxford, Oxford, United Kingdom.
  • Fehr J; Hasso-Plattner-Institute of Digital Engineering, Potsdam, Germany.
  • Leite AW; Machine Learning Laboratory in Finance and Organizations, Universidade de Brasília, Brasília, Brazil.
  • Goldschmidt PG; World Development Group Inc, Bethesda, MD, USA.
  • Johner C; Johner Institute, Konstanz, Germany.
  • Schörverth EDM; Fraunhofer HHI, Berlin, Germany.
  • Nakasi R; Makerere University, Kampala, Uganda.
  • Meyer M; Siemens Healthineers, Erlangen, Germany.
  • Cabitza F; University of Milano-Bicocca, Milan, Italy.
  • Baird P; Philips, New Kensington, USA.
  • Prabhu C; Office of the Auditor General of Norway, Oslo, Norway.
  • Weicken E; Fraunhofer HHI, Berlin, Germany.
  • Liu X; University Hospitals Birmingham NHS Foundation Trust & Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.
  • Wenzel M; Fraunhofer HHI, Berlin, Germany.
  • Vogler S; Bayer AG, Berlin, Germany.
  • Akogo D; minoHealth AI Labs, Accra, Ghana.
  • Alsalamah S; Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
  • Kazim E; Digital Health and Innovation Department, Science Division, World Health Organization, Winterthur, Switzerland.
  • Koshiyama A; University College London, London, United Kingdom.
  • Piechottka S; University College London, London, United Kingdom.
  • Macpherson S; Open Regulatory, Bonn, Germany.
  • Shadforth I; MIOTIFY LTD, London, United Kingdom.
  • Geierhofer R; MIOTIFY LTD, London, United Kingdom.
  • Matek C; IEC TC62 and Siemens Healthineers, Erlangen, Germany.
  • Krois J; Helmholtz Zentrum München, Neuherberg, Germany.
  • Sanguinetti B; Oral Diagnostics Digital Health Health Services Research, Charité-Universitätsmedizin, Berlin, Germany.
  • Arentz M; Dotphoton AG, Zug, Switzerland.
  • Bielik P; Department of Global Health, University of Washington, Washington, USA.
  • Calderon-Ramirez S; LatticeFlow & ETH Zurich, Zürich, Switzerland.
  • Abbood A; De Montfort University & Instituto Tecnologico de Costa Rica, Cartago, Costa Rica.
  • Langer N; Robert Koch Institut, Berlin, Germany.
  • Haufe S; Department of Psychology, University of Zurich, Zürich, Switzerland.
  • Kherif F; Technische Universität Berlin, Berlin, Germany.
  • Pujari S; Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Samek W; Digital Health and Innovation Department, Science Division, World Health Organization, Winterthur, Switzerland.
  • Wiegand T; Fraunhofer HHI, Berlin, Germany.
J Med Syst ; 45(12): 105, 2021 Nov 02.
Article in English | MEDLINE | ID: covidwho-1491288
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Quality Control / Algorithms / Machine Learning Limits: Humans Language: English Journal: J Med Syst Year: 2021 Document Type: Article Affiliation country: S10916-021-01783-y

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Quality Control / Algorithms / Machine Learning Limits: Humans Language: English Journal: J Med Syst Year: 2021 Document Type: Article Affiliation country: S10916-021-01783-y