Title
Machine Learning for Health: Algorithm Auditing & Quality Control
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.
Year
DOI
Venue
2021
10.1007/s10916-021-01783-y
JOURNAL OF MEDICAL SYSTEMS
Keywords
DocType
Volume
Machine learning, Artificial intelligence, Algorithm, Health, Auditing, Quality control
Journal
45
Issue
ISSN
Citations 
12
0148-5598
1
PageRank 
References 
Authors
0.36
0
39