Work

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

Year: 2023

Type: conference-paper

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.

Source: International Conference on Software Technologies (ICSOFT)

DOI: 10.5220/0012121300003538

Authors: Miriam Elia, Bernhard Bauer

Institution: University of Augsburg

Language: English

Topic: Safety Systems Engineering in Autonomy

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Bundesministerium für Bildung und Forschung