| Zugriffsnummer | 48699 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Machine learning for health: algorithm auditing and quality control |
| Autor(in); Institution |
Oala, Luis; Fraunhofer HHI, Berlin, GERMANY
Murchison, Andrew G.; Oxford University Hospitals NHS Foundation Trust, Oxford, UNITED KINGDOM
Balachandran, Pradeep; Technical Consultant (Digital Health), Thiruvananthapuram, INDIA
Choudhary, Shruti; University of Oxford, Oxford, UNITED KINGDOM
Fehr, Jana; Hasso-Plattner-Institute of Digital Engineering, Potsdam, GERMANY
Leite, Alixandro Werneck; Machine Learning Laboratory in Finance and Organizations, Universidade de Brasília, Brasília, BRAZIL
Goldschmidt, Peter G.; World Development Group Inc, Bethesda, MD, USA
Johner, Christian; Johner Institute, Konstanz, GERMANY
Schörverth, Elora D. M.; Fraunhofer HHI, Berlin, GERMANY
Nakasi, Rose; Makerere University, Kampala, UGANDA
Meyer, Martin; Siemens Healthineers, Erlangen, GERMANY
Cabitza, Federico; University of Milano-Bicocca, Milan, ITALY
Baird, Pat; Philips, New Kensington, USA
Prabhu, Carolin; Office of the Auditor General of Norway, Oslo, NORWAY
Weicken, Eva; Fraunhofer HHI, Berlin, GERMANY
Liu, Xiaoxuan; 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, Markus; Fraunhofer HHI, Berlin, GERMANY
Vogler, Steffen; Bayer AG, Berlin, GERMANY
Akogo, Darlington; minoHealth AI Labs, Accra, GHANA
|
| Quelle/Jahr | Journal of Medical Systems:(2021), 45, 1 - 8 |
| Artikelnummer | 105 |
| Availability | [online only] |
| ISSN | 1573-689X (ONLINE) |
| DOI | |
| Verlag | New York: Springer |
| Freie Schlagworte | Machine learning ; Artificial intelligence ; Algorithm ; Health ; Auditing ; Quality control |
| Zusammenfassung | 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. |
| Themenbereich der Metrologie | Metrologie in der Medizin |
| Weitere Informationen | 39 authors, not all individual authors are listed ; 31 institutions, not all institutions are listed |
Zitierung
Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., Meyer, M., Cabitza, F., Baird, P., Prabhu, C., Weicken, E., Liu, X., Wenzel, M., Vogler, S., Akogo, D., & Haufe, S. (2021). Machine learning for health: algorithm auditing and quality control. Journal of Medical Systems, 1–8. https://doi.org/10.1007/s10916-021-01783-y