Machine Learning for Health: Algorithm Auditing & Quality Control.

Oala, L.; Murchison, A.G.; Balachandran, P.; Choudhary, S.; Fehr, J.; Leite, A.W.; Goldschmidt, P.G.; Johner, C.; Schörverth, EDM; Nakasi, R.; Meyer, M.; Cabitza, F.; Baird, P.; Prabhu, C.; Weicken, E.; Liu, X.; Wenzel, M.; Vogler, S.; Akogo, D.; Alsalamah, S.; Kazim, E.; Koshiyama, A.; Piechottka, S.; Macpherson, S.; Shadforth, I.; Geierhofer, R.; Matek, C.; Krois, J.; Sanguinetti, B.; Arentz, M.; Bielik, P.; Calderon-Ramirez, S.; Abbood, A.; Langer, N.; Haufe, S.; Kherif, F.; Pujari, S.; Samek, W.; Wiegand, T.

doi:10.1007/s10916-021-01783-y

Machine Learning for Health: Algorithm Auditing & Quality Control.

Détails

Demande d'une copie Sous embargo indéterminé.
Accès restreint UNIL
Etat: Public
Version: de l'auteur⸱e
Licence: CC BY 4.0

ID Serval

serval:BIB_90CC2EB365C5

Type

Article: article d'un périodique ou d'un magazine.

Sous-type

Editorial

Collection

Publications

Institution

UNIL/CHUV

Titre

Machine Learning for Health: Algorithm Auditing & Quality Control.

Périodique

Journal of medical systems

Auteur⸱e⸱s

Oala L., Murchison A.G., Balachandran P., Choudhary S., Fehr J., Leite A.W., Goldschmidt P.G., Johner C., Schörverth EDM, Nakasi R., Meyer M., Cabitza F., Baird P., Prabhu C., Weicken E., Liu X., Wenzel M., Vogler S., Akogo D., Alsalamah S., Kazim E., Koshiyama A., Piechottka S., Macpherson S., Shadforth I., Geierhofer R., Matek C., Krois J., Sanguinetti B., Arentz M., Bielik P., Calderon-Ramirez S., Abbood A., Langer N., Haufe S., Kherif F., Pujari S., Samek W., Wiegand T.

ISSN

1573-689X (Electronic)

ISSN-L

0148-5598

Statut éditorial

Publié

Date de publication

02/11/2021

Peer-reviewed

Oui

Volume

Numéro

Pages

105

Langue

anglais

Notes

Publication types: Editorial
Publication Status: 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.

Mots-clé

Algorithms, Humans, Machine Learning, Quality Control, Algorithm, Artificial intelligence, Auditing, Health, Machine learning, Quality control

OAI-PMH

oai:serval.unil.ch:BIB_90CC2EB365C5

DOI

10.1007/s10916-021-01783-y

Pubmed

34729675

Web of science

000714023800001

Open Access

Oui