Decisions are not all equal—Introducing a utility metric based on case-wise raters' perceptions. (June 2022)

• Record Type: Journal Article
• Authors: Campagner, Andrea; Sternini, Federico; Cabitza, Federico
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: Utility -- Machine learning -- Validation -- Evaluation -- Human-centred -- Health informatics; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106930
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a human-centred utility metrics for the evaluation of medical AI systems. We show that the proposed metric generalizes existing proposals in the literature. We illustrate the application of our metric in three realistic medical case studies. We make the point for a human-centred approach to assess the impact of AI models. Abstract: Background and Objective Evaluation of AI-based decision support systems (AI-DSS) is of critical importance in practical applications, nonetheless common evaluation metrics fail to properly consider relevant and contextual information. In this article we discuss a novel utility metric, the weighted Utility (wU), for the evaluation of AI-DSS, which is based on the raters' perceptions of their annotation hesitation and of the relevance of the training cases. Methods We discuss the relationship between the proposed metric and other previous proposals; and we describe the application of the proposed metric for both model evaluation and optimization, through three realistic case studies. Results We show that our metric generalizes the well-known Net Benefit, as well as other common error-based and utility-based metrics. Through the empirical studies, we show that our metric can provide a more flexible tool for the evaluation of AI models. We also show that, compared to other optimization metrics, model optimization based on the w U can provide significantly better performance (AUC 0.862 vs 0.895, p-value < 0.05 ), especially on cases judged to be more complex by the human annotators (AUC 0.85 vs 0.92, p-value < 0.05 ). Conclusions We make the point for having utility as a primary concern in the evaluation and optimization of machine learning models in critical domains, like the medical one; and for the importance of a human-centred approach to assess the potential impact of AI models on human decision making also on the basis of further information that can be collected during the ground-truthing process.