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Machine Learning for Health: Algorithm Auditing & Quality Control
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 scree...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562935/ https://www.ncbi.nlm.nih.gov/pubmed/34729675 http://dx.doi.org/10.1007/s10916-021-01783-y |
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| author | 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 |
| author_facet | 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 |
| author_sort | Oala, Luis |
| collection | PubMed |
| description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-021-01783-y. |
| format | Online Article Text |
| id | pubmed-8562935 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-85629352021-11-03 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 J Med Syst Implementation Science & Operations Management 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-021-01783-y. Springer US 2021-11-02 2021 /pmc/articles/PMC8562935/ /pubmed/34729675 http://dx.doi.org/10.1007/s10916-021-01783-y Text en © The Author(s) 2021, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
| spellingShingle | Implementation Science & Operations Management 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 Machine Learning for Health: Algorithm Auditing & Quality Control |
| title | Machine Learning for Health: Algorithm Auditing & Quality Control |
| title_full | Machine Learning for Health: Algorithm Auditing & Quality Control |
| title_fullStr | Machine Learning for Health: Algorithm Auditing & Quality Control |
| title_full_unstemmed | Machine Learning for Health: Algorithm Auditing & Quality Control |
| title_short | Machine Learning for Health: Algorithm Auditing & Quality Control |
| title_sort | machine learning for health: algorithm auditing & quality control |
| topic | Implementation Science & Operations Management |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562935/ https://www.ncbi.nlm.nih.gov/pubmed/34729675 http://dx.doi.org/10.1007/s10916-021-01783-y |
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