{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:20:18Z","timestamp":1781623218522,"version":"3.54.5"},"reference-count":98,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["964220"],"award-info":[{"award-number":["964220"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>This article offers an extensive survey of one of the fundamental aspects of the trustworthiness of AI in healthcare, namely uncertainty, focusing on the large panoply of recent studies addressing the connection between uncertainty, AI, and healthcare. The concept of uncertainty is a recurring theme across multiple disciplines, with varying focuses and approaches. Here, we focus on the diverse nature of uncertainty in medical applications, emphasizing the importance of quantifying uncertainty in model predictions and its advantages in specific clinical settings. Questions that emerge in this context range from the guidelines for AI integration in the healthcare domain to the ethical deliberations and their compatibility with cutting-edge AI research. Together with a description of the main specific works in this context, we also discuss that, as medicine evolves and introduces novel sources of uncertainty, there is a need for more versatile uncertainty quantification methods to be developed collaboratively by researchers and healthcare professionals. Finally, we acknowledge the limitations of current uncertainty quantification methods in addressing the different facets of uncertainty within the medical domain. In particular, we identify from this survey a relative paucity of approaches that focus on the user\u2019s perception of uncertainty and accordingly of trustworthiness.<\/jats:p>","DOI":"10.1145\/3716317","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T15:59:14Z","timestamp":1738684754000},"page":"1-32","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Navigating Uncertainty: A User-Perspective Survey of Trustworthiness of AI in Healthcare"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0004-1148-3789","authenticated-orcid":false,"given":"Jaya","family":"Ojha","sequence":"first","affiliation":[{"name":"Oslo Metropolitan University, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0005-6909-4476","authenticated-orcid":false,"given":"Oriana","family":"Presacan","sequence":"additional","affiliation":[{"name":"Oslo Metropolitan University, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-8176-666X","authenticated-orcid":false,"given":"Pedro","family":"G. Lind","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Oslo Metropolitan University, Oslo, Norway and Simula Research Laboratory, Numerical Analysis and Scientific Computing, Oslo, Norway, and Kristiania University of Applied Sciences, Oslo, NorwayNorway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-1139-7317","authenticated-orcid":false,"given":"Eric","family":"Monteiro","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology, Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-7591-1659","authenticated-orcid":false,"given":"Anis","family":"Yazidi","sequence":"additional","affiliation":[{"name":"Oslo Metropolitan University, Oslo, Norway and Norwegian University of Science and Technology, Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"O. L. Dictionaries. Trust. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/www.oxfordlearnersdictionaries.com\/definition\/english\/trust_1"},{"key":"e_1_3_1_3_2","unstructured":"What Is AI? What Does Artificial Intelligence Do? 2019. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/www.bbc.co.uk\/newsround\/49274918"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12525-020-00441-4"},{"key":"e_1_3_1_5_2","first-page":"37","volume-title":"International Conference on Artificial Intelligence and Computer Vision (AICV \u201920)","author":"Alhashmi S. F.","year":"2020","unstructured":"S. F. Alhashmi, M. Alshurideh, B. Al Kurdi, and S. A. Salloum. 2020. A systematic review of the factors affecting the artificial intelligence implementation in the health care sector. In International Conference on Artificial Intelligence and Computer Vision (AICV \u201920). Springer, 37\u201349."},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/B978-0-12-824557-6.00014-5","volume-title":"Cyber-Physical Systems","author":"Saxena S.","year":"2022","unstructured":"S. Saxena and Amritesh. 2022. Evolving uncertainty in healthcare service interactions during Covid-19: Artificial intelligence-A threat or support to value cocreation? In Cyber-Physical Systems. Ramesh Chandra Poonia, Basant Agarwal, Sandeep Kumar, Mohammad S. Khan, Gon\u00e7alo Marques, and Janmenjoy Nayak (Eds.), Elsevier, 93\u2013116."},{"key":"e_1_3_1_7_2","unstructured":"K. Johnson. 2021. Confidence Uncertainty and Trust in AI Affect How Humans Make Decisions. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/venturebeat.com\/ai\/confidence-uncertainty-and-trust-in-ai-affect-how-humans-make-decisions\/"},{"issue":"578","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"eaba4373","DOI":"10.1126\/scitranslmed.aba4373","article-title":"Toward robust mammography-based models for breast cancer risk","volume":"13","author":"Yala A.","year":"2021","unstructured":"A. Yala, P. G. Mikhael, F. Strand, G. Lin, K. Smith, Y.-L. Wan, L. Lamb, K. Hughes, C. Lehman, and R. Barzilay. 2021. Toward robust mammography-based models for breast cancer risk. Science Translational Medicine 13, 578 (2021), eaba4373.","journal-title":"Science Translational Medicine"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107555"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae060"},{"key":"e_1_3_1_11_2","volume-title":"FAT Flow: A Data Science Ethics Framework","author":"Martens D.","year":"2020","unstructured":"D. Martens. 2020. FAT Flow: A Data Science Ethics Framework. University of Antwerp, Faculty of Business and Economics."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.07.030"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"107441","DOI":"10.1016\/j.compbiomed.2023.107441","article-title":"Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013\u20132023)","volume":"165","author":"Seoni S.","year":"2023","unstructured":"S. Seoni, V. Jahmunah, M. Salvi, P. D. Barua, F. Molinari, and U. R. Acharya. 2023. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013\u20132023). Computers in Biology and Medicine 165 (2023), 107441.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2019.00721"},{"key":"e_1_3_1_16_2","unstructured":"K. Zou Z. Chen X. Yuan X. Shen M. Wang and H. Fu. 2023. A review of uncertainty estimation and its application in medical imaging. arXiv:2302.08119. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2302.08119"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/978-3-031-26490-0_16","volume-title":"New Digital Work: Digital Sovereignty at the Workplace","author":"Wu X.","year":"2023","unstructured":"X. Wu, P. Wagner, and M. F. Huber. 2023. Quantification of uncertainties in neural networks. In New Digital Work: Digital Sovereignty at the Workplace. Shajek Alexandra and Andreas Ernst (Eds.), Springer International Publishing, Cham, 276\u2013287."},{"key":"e_1_3_1_18_2","unstructured":"A. Shamsi H. Asgharnezhad M. Abdar A. Tajally A. Khosravi S. Nahavandi and H. Leung. 2021. Improving mc-dropout uncertainty estimates with calibration error-based optimization. arXiv:2110.03260. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2110.03260"},{"key":"e_1_3_1_19_2","unstructured":"S. Fort H. Hu and B. Lakshminarayanan. 2019. Deep ensembles: A loss landscape perspective. arXiv:1912.02757. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/1912.02757"},{"key":"e_1_3_1_20_2","unstructured":"W. He Z. Jiang T. Xiao Z. Xu and Y. Li. 2023. A survey on uncertainty quantification methods for deep learning. arXiv:2302.13425. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2302.13425"},{"key":"e_1_3_1_21_2","first-page":"133","article-title":"Training multilayer perceptrons with the extended Kalman algorithm","volume":"1","author":"Singhal S.","year":"1988","unstructured":"S. Singhal and L. Wu. 1988. Training multilayer perceptrons with the extended Kalman algorithm. In Advances in Neural Information Processing Systems, Vol. 1, 133\u2013140.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/BF00439421","article-title":"Learning algorithms for neural networks with the Kalman filters","volume":"3","author":"Watanabe K.","year":"1990","unstructured":"K. Watanabe and S. G. Tzafestas. 1990. Learning algorithms for neural networks with the Kalman filters. Journal of Intelligent and Robotic Systems 3, 4 (1990), 305\u2013319.","journal-title":"Journal of Intelligent and Robotic Systems"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.006"},{"key":"e_1_3_1_24_2","first-page":"1","article-title":"Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991\u20132020)","volume":"399","author":"Alizadehsani R.","year":"2021","unstructured":"R. Alizadehsani, M. Roshanzamir, S. Hussain, A. Khosravi, A. Koohestani, M. H. Zangooei, M. Abdar, A. Beykikhoshk, A. Shoeibi, A. Zare, et al. 2021. Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991\u20132020). Annals of Operations Research 399 (2021), 1\u201342.","journal-title":"Annals of Operations Research"},{"key":"e_1_3_1_25_2","unstructured":"J. Yang K. Zhou Y. Li and Z. Liu. 2021. Generalized out-of-distribution detection: A survey. arXiv:2110.11334. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2110.11334"},{"key":"e_1_3_1_26_2","first-page":"5","volume-title":"30th Conference on Neural Information Processing Systems","author":"Lakkaraju H.","year":"2016","unstructured":"H. Lakkaraju, E. Kamar, R. Caruana, and E. Horvitz. 2016. Discovering unknown unknowns of predictive models. In 30th Conference on Neural Information Processing Systems, 5."},{"key":"e_1_3_1_27_2","unstructured":"T. U. Project. 2024. Rumsfeld Matrix. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/www.theuncertaintyproject.org\/tools\/rumsfeld-matrix"},{"key":"e_1_3_1_28_2","volume-title":"The Impact of the Highly Improbable","author":"Taleb N. N.","year":"2008","unstructured":"N. N. Taleb. 2008. The Impact of the Highly Improbable. Penguin Books Limited."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1093\/cje\/bex035"},{"issue":"1","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s12911-016-0377-1","article-title":"Cognitive biases associated with medical decisions: A systematic review","volume":"16","author":"Saposnik G.","year":"2016","unstructured":"G. Saposnik, D. Redelmeier, C. C. Ruff, and P. N. Tobler. 2016. Cognitive biases associated with medical decisions: A systematic review. BMC Medical Informatics and Decision Making 16, 1 (2016), 138\u2013114.","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"e_1_3_1_31_2","first-page":"1321","volume-title":"International Conference on Machine Learning","author":"Guo C.","year":"2017","unstructured":"C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. 2017. On calibration of modern neural networks. In International Conference on Machine Learning. PMLR, 1321\u20131330."},{"key":"e_1_3_1_32_2","unstructured":"A. Mehta. 2024. Platt Scaling & Calibration. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/medium.com\/@amehta1\\_be20\/platt-scaling-calibration-0121d4761297#::text=Platt%20scaling%20is%20essentially%20a train%20a%20Logistic%20Regression%20Model"},{"key":"e_1_3_1_33_2","first-page":"694","volume-title":"8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zadrozny B.","year":"2002","unstructured":"B. Zadrozny and C. Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 694\u2013699."},{"key":"e_1_3_1_34_2","first-page":"2901","volume-title":"AAAI Conference on Artificial Intelligence","volume":"29","author":"Naeini M. P.","year":"2015","unstructured":"M. P. Naeini, G. Cooper, and M. Hauskrecht. 2015. Obtaining well calibrated probabilities using Bayesian binning. In AAAI Conference on Artificial Intelligence, Vol. 29, 2901\u20132907."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107816"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/app14020675"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105894"},{"key":"e_1_3_1_38_2","first-page":"145","volume-title":"International Conference on Artificial Intelligence in Medicine","author":"L\u00f6hr T.","year":"2024","unstructured":"T. L\u00f6hr, M. Ingrisch, and E. H\u00fcllermeier. 2024. Towards aleatoric and epistemic uncertainty in medical image classification. In International Conference on Artificial Intelligence in Medicine. Springer, 145\u2013155."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.103"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-022-00988-x"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/TMI.2024.3445999","article-title":"Investigating and improving latent density segmentation models for aleatoric uncertainty quantification in medical imaging","volume":"44","author":"Valiuddin M. A.","year":"2024","unstructured":"M. A. Valiuddin, C. G. Viviers, R. J. Van Sloun, P. H. De With, and F. van der Sommen. 2024. Investigating and improving latent density segmentation models for aleatoric uncertainty quantification in medical imaging. IEEE Transactions on Medical Imaging 44 (2024), 384\u2013395.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3163384"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2019.106816"},{"key":"e_1_3_1_44_2","first-page":"691","volume-title":"21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI \u201918)","author":"Eaton-Rosen Z.","year":"2018","unstructured":"Z. Eaton-Rosen, F. Bragman, S. Bisdas, S. Ourselin, and M. J. Cardoso. 2018. Towards safe deep learning: Accurately quantifying biomarker uncertainty in neural network predictions. In 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI \u201918). Springer, 691\u2013699."},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1109\/ISBI48211.2021.9433954","volume-title":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","author":"Li Y.","year":"2021","unstructured":"Y. Li, X. Chen, L. Quan, and N. Zhang. 2021. Uncertainty-guided robust training for medical image segmentation. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 1471\u20131475."},{"key":"e_1_3_1_46_2","volume-title":"IOE Graduate Conference","author":"Dhakal P.","year":"2021","unstructured":"P. Dhakal and S. R. Joshi. 2021. Uncertainty estimation in detecting knee abnormalities on MRI using Bayesian deep learning. In IOE Graduate Conference."},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2023.07.052"},{"key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"85442","DOI":"10.1109\/ACCESS.2021.3085418","article-title":"Improving uncertainty estimation with semi-supervised deep learning for Covid-19 detection using chest x-ray images","volume":"9","author":"Calderon-Ramirez Saul","year":"2021","unstructured":"Saul Calderon-Ramirez, Shengxiang Yang, Armaghan Moemeni, Simon Colreavy-Donnelly, David A. Elizondo, Luis Oala, Jorge Rodriguez-Capitan, Manuel Jimenez-Navarro, Ezequiel Lopez-Rubio, and Miguel A. Molina-Cabello. 2021. Improving uncertainty estimation with semi-supervised deep learning for Covid-19 detection using chest x-ray images. IEEE Access: Practical Innovations, Open Solutions 9 (2021), 85442\u201385454.","journal-title":"IEEE Access: Practical Innovations, Open Solutions"},{"issue":"2","key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"e24188","DOI":"10.1016\/j.heliyon.2024.e24188","article-title":"Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification: A ranking-based approach","volume":"10","author":"Abdullah A. A.","year":"2024","unstructured":"A. A. Abdullah, M. M. Hassan, and Y. T. Mustafa. 2024. Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification: A ranking-based approach. Heliyon 10, 2 (2024), e24188.","journal-title":"Heliyon"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-023-09710-2"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2020.11.005"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101978"},{"key":"e_1_3_1_53_2","unstructured":"K. Bykov M. M. C. H\u00f6hne K.-R. M\u00fcller S. Nakajima and M. Kloft. 2020. How much can I trust you? Quantifying uncertainties in explaining neural networks. arXiv:2006.09000. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2006.09000"},{"key":"e_1_3_1_54_2","unstructured":"A. Filos S. Farquhar A. N. Gomez T. G. Rudner Z. Kenton L. Smith M. Alizadeh A. De Kroon and Y. Gal. 2019. A systematic comparison of Bayesian deep learning robustness in diabetic retinopathy tasks. arXiv:1912.10481. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/1912.10481"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm8081241"},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/ICHI52183.2021.00022","volume-title":"2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)","author":"Wu Z.","year":"2021","unstructured":"Z. Wu, Y. Yang, J. Gu, and V. Tresp. 2021. Quantifying predictive uncertainty in medical image analysis with deep kernel learning. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). IEEE, 63\u201372."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101955"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0289930"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107758"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-022-10785-x"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0276250"},{"key":"e_1_3_1_62_2","first-page":"347","volume-title":"Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence","author":"Xia T.","year":"2022","unstructured":"T. Xia, J. Han, and C. Mascolo. 2022. Benchmarking uncertainty quantification on biosignal classification tasks under dataset shift. In Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence. Shaban-Nejad Arash, Michalowski Martin, and Bianco Simone (Eds.), Springer, 347\u2013359."},{"key":"e_1_3_1_63_2","first-page":"122","volume-title":"3rd International Workshop Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (UNSURE \u201921), and 6th International Workshop (PIPPI \u201921), Held in Conjunction with MICCAI \u201921","author":"Berger C.","year":"2021","unstructured":"C. Berger, M. Paschali, B. Glocker, and K. Kamnitsas. 2021. Confidence-based out-of-distribution detection: A comparative study and analysis. In 3rd International Workshop Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (UNSURE \u201921), and 6th International Workshop (PIPPI \u201921), Held in Conjunction with MICCAI \u201921. Springer, 122\u2013132."},{"key":"e_1_3_1_64_2","unstructured":"D. Karimi and A. Gholipour. 2020. Improving calibration and out-of-distribution detection in medical image segmentation with convolutional neural networks. arXiv:2004.06569. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2004.06569"},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1145\/3485447.3512040","volume-title":"the ACM Web Conference 2022","author":"Noorian S. Sharifi","year":"2022","unstructured":"S. Sharifi Noorian, S. Qiu, U. Gadiraju, J. Yang, and A. Bozzon. 2022. What should you know? A human-in-the-loop approach to unknown unknowns characterization in image recognition. In the ACM Web Conference 2022, 882\u2013892."},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3576050.3576148"},{"issue":"3","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3946\/kjme.2018.92","article-title":"Understanding uncertainty in medicine: Concepts and implications in medical education","volume":"30","author":"Kim K.","year":"2018","unstructured":"K. Kim and Y.-M. Lee. 2018. Understanding uncertainty in medicine: Concepts and implications in medical education. Korean Journal of Medical Education 30, 3 (2018), 181\u2013188.","journal-title":"Korean Journal of Medical Education"},{"key":"e_1_3_1_68_2","unstructured":"C. Clinic. 2023. Precision Medicine. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/my.clevelandclinic.org\/health\/articles\/precision-medicine"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1111\/jep.13789"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000085"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462571"},{"key":"e_1_3_1_72_2","volume-title":"Communicating Uncertain Information from Deep Learning Models in Human Machine Teams","author":"Subramanian H. V.","year":"2020","unstructured":"H. V. Subramanian, C. I. Canfield, D. B. Shank, L. Andrews, and C. H. Dagli. 2020. Communicating Uncertain Information from Deep Learning Models in Human Machine Teams. American Society for Engineering Management (ASEM)."},{"key":"e_1_3_1_73_2","volume-title":"Communicating Uncertain Information from Deep Learning Models to Users","author":"Subramanian H. V.","year":"2021","unstructured":"H. V. Subramanian. 2021. Communicating Uncertain Information from Deep Learning Models to Users. Missouri University of Science and Technology."},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300391"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2021\/16564"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.2196\/15154"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3631614"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43095-4"},{"key":"e_1_3_1_79_2","first-page":"1453","volume-title":"Medical Imaging with Deep Learning","author":"Mehta R.","year":"2024","unstructured":"R. Mehta, C. Shui, and T. Arbel. 2024. Evaluating the fairness of deep learning uncertainty estimates in medical image analysis. In Medical Imaging with Deep Learning. Oguz Ipek, Noble Jack, Li Xiaoxiao, Styner Martin, Baumgartner Christian, Rusu Mirabela, Heinmann Tobias, Kontos Despina, Landman Bennett and Dawant Benoit (Eds.), PMLR, 1453\u20131492."},{"key":"e_1_3_1_80_2","unstructured":"S. V. Chinta Z. Wang X. Zhang T. D. Viet A. Kashif M. A. Smith and W. Zhang. 2024. AI-driven healthcare: A survey on ensuring fairness and mitigating bias. arXiv:2407.19655. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/arxiv.org\/abs\/2407.19655"},{"key":"e_1_3_1_81_2","article-title":"Who issues first global report on artificial intelligence (AI) in health and six guiding principles for its design and use","volume":"28","author":"World Health Organization","year":"2021","unstructured":"World Health Organization. 2021. Who issues first global report on artificial intelligence (AI) in health and six guiding principles for its design and use. World Health Organization, Vol. 28.","journal-title":"World Health Organization"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-021-00131-7"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3577009"},{"key":"e_1_3_1_84_2","unstructured":"N. Reiff. 2023. What Is ChatGPT? The AI Natural Language Processing Tool Explained. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/decrypt.co\/resources\/what-is-chatgpt"},{"key":"e_1_3_1_85_2","doi-asserted-by":"crossref","first-page":"108013","DOI":"10.1016\/j.cmpb.2024.108013","article-title":"ChatGPT in healthcare: A taxonomy and systematic review","volume":"245","author":"Li J.","year":"2024","unstructured":"J. Li, A. Dada, B. Puladi, J. Kleesiek, and J. Egger. 2024. ChatGPT in healthcare: A taxonomy and systematic review. Computer Methods and Programs in Biomedicine 245 (2024), 108013.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_3_1_86_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.25054"},{"key":"e_1_3_1_87_2","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ajem.2024.01.037","article-title":"Assessing the precision of artificial intelligence in emergency department triage decisions: Insights from a study with ChatGPT","volume":"78","author":"Pasl\u0131 S.","year":"2024","unstructured":"S. Pasl\u0131, A. S. \u015eahin, M. F. Be\u015fer, H. Top\u00e7uo\u011flu, M. Yadigaro\u011flu, and M. \u0130mamo\u011flu. 2024. Assessing the precision of artificial intelligence in emergency department triage decisions: Insights from a study with ChatGPT. The American Journal of Emergency Medicine 78 (2024), 170\u2013175.","journal-title":"The American Journal of Emergency Medicine"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.38053\/acmj.1367414"},{"key":"e_1_3_1_89_2","first-page":"1","article-title":"ChatGPT and the healthcare industry: A comprehensive analysis of its impact on medical writing","author":"Awal S. S.","year":"2023","unstructured":"S. S. Awal and S. S. Awal. 2023. ChatGPT and the healthcare industry: A comprehensive analysis of its impact on medical writing. Journal of Public Health (2023), 1\u20134.","journal-title":"Journal of Public Health"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10676-022-09630-5"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11948-020-00228-y"},{"key":"e_1_3_1_92_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-022-00241-w"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1177\/10596011231160574"},{"key":"e_1_3_1_94_2","doi-asserted-by":"publisher","DOI":"10.3390\/make6020058"},{"key":"e_1_3_1_95_2","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/978-3-030-86365-4_54","volume-title":"30th International Conference on Artificial Neural Networks and Machine Learning (ICANN \u201921)","author":"Yang S.","year":"2021","unstructured":"S. Yang and T. Fevens. 2021. Uncertainty quantification and estimation in medical image classification. In 30th International Conference on Artificial Neural Networks and Machine Learning (ICANN \u201921). Springer, 671\u2013683."},{"key":"e_1_3_1_96_2","first-page":"75","volume-title":"3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (UNSURE \u201921), and 6th International Workshop (PIPPI \u201921), Held in Conjunction with MICCAI \u201921","author":"Valiuddin M. A.","year":"2021","unstructured":"M. A. Valiuddin, C. G. Viviers, R. J. van Sloun, P. H. de With, and F. van der Sommen. 2021. Improving aleatoric uncertainty quantification in multi-annotated medical image segmentation with normalizing flows. In 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (UNSURE \u201921), and 6th International Workshop (PIPPI \u201921), Held in Conjunction with MICCAI \u201921. Springer, 75\u201388."},{"key":"e_1_3_1_97_2","first-page":"119","volume-title":"22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI \u201919)","author":"Baumgartner C. F.","year":"2019","unstructured":"C. F. Baumgartner, K. C. Tezcan, K. Chaitanya, A. M. H\u00f6tker, U. J. Muehlematter, K. Schawkat, A. S. Becker, O. Donati, and E. Konukoglu. 2019. PHiSeg: Capturing uncertainty in medical image segmentation. In 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI \u201919). Springer, 119\u2013127."},{"key":"e_1_3_1_98_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.59275\/j.melba.2022-354b","article-title":"Qu-brats: Miccai brats 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results","volume":"1","author":"Mehta R.","year":"2022","unstructured":"R. Mehta, A. Filos, U. Baid, C. Sako, R. McKinley, M. Rebsamen, K. D\u00e4twyler, R. Meier, P. Radojewski, G. K. Murugesan, et al. 2022. Qu-brats: Miccai brats 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results. Machine Learning for Biomedical Imaging 1 (Aug. 2022), 1\u201354.","journal-title":"Machine Learning for Biomedical Imaging"},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2020.1751866"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3716317","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3716317","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:43:43Z","timestamp":1750272223000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3716317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":98,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,7,31]]}},"alternative-id":["10.1145\/3716317"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3716317","relation":{},"ISSN":["2637-8051"],"issn-type":[{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]},"assertion":[{"value":"2024-04-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}