A Methodology Based on Quality Gates for Certifiable AI in Medicine: Towards a Reliable Application of Metrics in Machine Learning

Miriam Elia, Bernhard Bauer

2023

Abstract

As of now, intelligent technologies experience a rapid growth. For a reliable adoption of those new and powerful systems into day-to-day life, especially with respect to high-risk settings such as medicine, technical means to realize legal requirements correctly, are indispensible. Our proposed methodology comprises an approach to translate such partly more abstract concepts into concrete instructions - it is based on Quality Gates along the intelligent system’s complete life cycle, which are composed of use-case adapted Criteria that need to be addressed with respect to certification. Also, the underlying philosophy regarding stakeholder inclusion, domain embedding and risk analysis is illustrated. In the present paper, the Quality Gate Metrics is outlined for the application of machine learning performance metrics focused on binary classification.

Paper Citation

in Harvard Style

Elia M. and Bauer B. (2023). A Methodology Based on Quality Gates for Certifiable AI in Medicine: Towards a Reliable Application of Metrics in Machine Learning. In Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-665-1, SciTePress, pages 486-493. DOI: 10.5220/0012121300003538

in Bibtex Style

@conference{icsoft23,
author={Miriam Elia and Bernhard Bauer},
title={A Methodology Based on Quality Gates for Certifiable AI in Medicine: Towards a Reliable Application of Metrics in Machine Learning},
booktitle={Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2023},
pages={486-493},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012121300003538},
isbn={978-989-758-665-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT
TI - A Methodology Based on Quality Gates for Certifiable AI in Medicine: Towards a Reliable Application of Metrics in Machine Learning
SN - 978-989-758-665-1
AU - Elia M.
AU - Bauer B.
PY - 2023
SP - 486
EP - 493
DO - 10.5220/0012121300003538
PB - SciTePress