cirpublications.com-AFnB-1790899045-u679026846 1790899045 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2023 3 2 Anna Kowalska Piotr Nowak Tomasz Zielinski Katarzyna Mazur 2023 10.68159/u679026846 https://cirpublications.com/pub/journal/2/article/u679026846 Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283-6. 10.1056/NEJMc2104626 Albuquerque I, Monteiro J, Rosanne O, Falk TH. Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment. Front Artif Intell. 2022;5:992732. 10.3389/frai.2022.992732 Choi Y, Yu W, Nagarajan MB, Teng P, Goldin JG, Raman SS, et al. Translating AI to clinical practice: overcoming data shift with explainability. Radiographics. 2023;43(5):e220105. 10.1148/rg.220105 Subbaswamy A, Adams R, Saria S. Evaluating model robustness and stability to dataset shift. Proc Mach Learn Res. 2021;130:2611-9. Rahman A, Venkatakrishnan R, Sikder N, Janat M, Alrefai S, Ahammad M, et al. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Cluster Comput. 2023;26(4):2271-84. 10.1007/s10586-022-03658-4 Saraswat D, Verma A, Bhattacharya P, Prasad D, Tanveer M, Gupta S, et al. Explainable AI for Healthcare 5.0: opportunities and challenges. IEEE Access. 2022;10:84486-517. 10.1109/ACCESS.2022.3197671 London AJ. Artificial intelligence in medicine: overcoming or recapitulating structural challenges to improving patient care? Cell Rep Med. 2022;3(5):100622. 10.1016/j.xcrm.2022.100622 Shi Y, Li X, Wang S, Chen B. A multi-task unsupervised domain adaptation network for medical image segmentation. Proc SPIE. 2021;11928:1192802. 10.1117/12.2611637 Perkonigg M, Hofmanninger J, Herold CJ, Brink JA, Pianykh O, Prosch H, et al. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nat Commun. 2021;12(1):5678. 10.1038/s41467-021-25858-z Zhou C, Wang J, Wang Z, Wang J. Domain adaptation for medical image classification without source data. IEEE J Biomed Health Inform. 2022;26(8):3837-48. 10.1109/JBHI.2022.3210444 Zhang L, Wang X, Yang D, Sanford T, Harmon S, Turkbey B, et al. Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans Med Imaging. 2020;39(7):2531-40. 10.1109/TMI.2020.2973595 Diao P, Pai A, Igel C, Krag CH. Histogram-based unsupervised domain adaptation for medical image classification. In: Med Image Comput Comput Assist Interv. 2022;13437:755-64. 10.1007/978-3-031-16449-1_72 Yu Q, Xi N, Yuan J, Zhou Z, Dang K, Ding X. Source-free domain adaptation for medical image segmentation via prototype-anchored feature alignment and contrastive learning. In: Med Image Comput Comput Assist Interv. 2023;14226:3-12. 10.1007/978-3-031-43990-2_1 Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, et al. AI applications to medical images: from machine learning to deep learning. Phys Med. 2021;83:9-24. 10.1016/j.ejmp.2021.02.006 Franco-Barranco D, Pastor-Tronch J, González-Marfil A, Muñoz-Barrutia A, Arganda-Carreras I. Deep learning based domain adaptation for mitochondria segmentation on EM volumes. Comput Methods Programs Biomed. 2022;222:106949. 10.1016/j.cmpb.2022.106949 Guan H, Liu M. Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng. 2022;69(3):1173-85. 10.1109/TBME.2021.3114620 Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. 2021;5(6):493-7. 10.1038/s41551-021-00751-8 Bondi-Kelly E, Bondi-Kelly D, Michalska S, Rodriguez C, Taylor J, Sgaier K, et al. Taking off with AI: lessons from aviation for healthcare. Proc ACM Conf Equity Access Algorithms Mech Optim. 2023:1-11. 10.1145/3617694.3623224 Kobo-Greenhut A, Greenhut I, Sharvit L, Orlov O, Lavi D, Wasserman A, et al. Algorithmic prediction of failure modes in healthcare. Int J Qual Health Care. 2021;33(1):mzaa151. Phelps G, Cooper P. Can artificial intelligence help improve the quality of healthcare? J Hosp Manag Health Policy. 2020;4:29. 10.21037/jhmhp-20-115 Leeftink AG, Visser J, de Laat JM, van der Meij NTM, Vos JBH, Valk GD. Reducing failures in daily medical practice: healthcare failure mode and effect analysis combined with computer simulation. Ergonomics. 2021;64(9):1322-32. 10.1080/00140139.2021.1910734 Gichoya JW, Thomas K, Celi LA, Safdar N, Banerjee I, Banja JD, et al. AI pitfalls and what not to do: mitigating bias in AI. Br J Radiol. 2023;96(1150):20230023. 10.1259/bjr.20230023 Al Shehhi K, Khan S, Alaraj H. Fuzzy failure modes effect and criticality analysis of the procurement process of artificial intelligent systems/services. Artif Intell Robot Manuf. 2023;1(1):1-13. 10.37256/airm.1120232600 Kim MJ, Kim SH, Kim SM, Nam JH, Hwang YB, Lim YJ. The advent of domain adaptation into artificial intelligence for gastrointestinal endoscopy and medical imaging. Diagnostics. 2023;13(19):3023. 10.3390/diagnostics13193023 Chaddad A, Lu Q, Li J, Katib Y, Kateb R, Tanougast C, et al. Explainable, domain-adaptive, and federated artificial intelligence in medicine. IEEE CAA J Autom Sin. 2023;10(3):665-83. 10.1109/JAS.2023.123123 Guo LL, Pfohl SR, Fries J, Johnson AEW, Posada J, Aftandilian C, et al. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci Rep. 2022;12(1):2726. 10.1038/s41598-022-06484-1 Guan H, Liu M. DomainATM: domain adaptation toolbox for medical data analysis. Neuroimage. 2023;268:119863. 10.1016/j.neuroimage.2023.119863 Zhu Y, Venugopalan J, Zhang Z, Chanani NK, Maher KO, Wang MD. Domain adaptation using convolutional autoencoder and gradient boosting for adverse events prediction in the intensive care unit. Front Artif Intell. 2022;5:640926. 10.3389/frai.2022.640926 Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: a simple and effective framework for domain adaptation. Med Image Anal. 2022;76:102309. 10.1016/j.media.2021.102309