Machine Learning for Health: Algorithm Auditing & Quality Control.
Algorithm
Artificial intelligence
Auditing
Health
Machine learning
Quality control
Journal
Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056
Informations de publication
Date de publication:
02 Nov 2021
02 Nov 2021
Historique:
received:
03
09
2021
accepted:
11
10
2021
entrez:
3
11
2021
pubmed:
4
11
2021
medline:
5
11
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34729675
doi: 10.1007/s10916-021-01783-y
pii: 10.1007/s10916-021-01783-y
pmc: PMC8562935
doi:
Types de publication
Editorial
Langues
eng
Sous-ensembles de citation
IM
Pagination
105Subventions
Organisme : World Health Organization
ID : 001
Pays : International
Informations de copyright
© 2021. The Author(s).
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