Authors:
Chiara Natali
1
;
Andrea Campagner
2
and
Federico Cabitza
1
;
2
Affiliations:
1
Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milan, Italy
;
2
IRCCS Ospedale Galeazzi - Sant’Ambrogio, Milan, Italy
Keyword(s):
Medical Machine Learning, Decision Support Systems, Validation, Assessment.
Abstract:
The research about, and use of, AI-based Decision Support Systems (DSS) has been steadily increasing in the recent years: however, tools and techniques to validate and evaluate these systems in an holistic manner are still largely lacking, especially in regard to their potential impact on actual human decision-making. This paper challenges the accuracy-centric paradigm in DSS evaluation by introducing the nuanced, multi-dimensional approach of the DSS Quality Assessment Tool. Developed at MUDI Lab (University of Milano-Bicocca), this free, open-source tool supports the quality assessment of AI-based decision support systems (DSS) along six different and complementary dimensions: model robustness, data similarity, calibration, utility, data reliability and impact on human decision making. Each dimension is analyzed for its relevance in the Medical AI domain, the metrics employed, and their visualizations, designed according to the principle of vague visualizations to promote cognitive
engagement. Such a tool can be instrumental to foster a culture of continuous oversight, outcome monitoring, and reflective technology assessment.
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