bibtext: @InProceedings{ Kon_Aframework_MICCAI2024,
                   author = { Konuk, Emir and Welch, Robert and Christiansen, Filip and Epstein, Elisabeth and Smith, Kevin },
                   title = { { A framework for assessing joint human-AI systems based on uncertainty estimation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: We investigate the role of uncertainty quantification in aiding medical decision-making. Existing evaluation metrics fail to capture the practical utility of joint human-AI decision-making systems. To address this, we introduce a novel framework to assess such systems and use it to benchmark a diverse set of confidence and uncertainty estimation methods. Our results show that certainty measures enable joint human-AI systems to outperform both standalone humans and AIs, and that for a given system there exists an optimal balance in the number of cases to refer to humans, beyond which the system’s performance degrades.
title: A framework for assessing joint human-AI systems based on uncertainty estimation
authors: Konuk, Emir Welch, Robert Christiansen, Filip Epstein, Elisabeth Smith, Kevin