Oala, L;
Murchison, AG;
Balachandran, P;
Choudhary, S;
Fehr, J;
Leite, AW;
Goldschmidt, PG;
... Wiegand, T; + view all
(2021)
Machine Learning for Health: Algorithm Auditing & Quality Control.
Journal of Medical Systems
, 45
(12)
, Article 105. 10.1007/s10916-021-01783-y.
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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.
| Type: | Article |
|---|---|
| Title: | Machine Learning for Health: Algorithm Auditing & Quality Control |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10916-021-01783-y |
| Publisher version: | https://doi.org/10.1007/s10916-021-01783-y |
| Language: | English |
| Additional information: | © 2021 Springer Nature Switzerland AG.This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Machine learning, Artificial intelligence, Algorithm, Health, Auditing, Quality control |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139395 |
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