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Surv."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can improve the accuracy, robustness, and privacy of downstream AI algorithms. However, access to high-quality datasets is limited by the technical difficulties of data acquisition, and large-scale sharing of healthcare data is hindered by strict ethical restrictions. Data synthesis algorithms, which generate data with distributions similar to real clinical data, can serve as a potential solution to address the scarcity of good quality data during the development of trustworthy AI. However, state-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Therefore, in this article, we will review synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-style review article will provide comprehensive descriptions of non-imaging medical data synthesis, covering aspects such as algorithms, evaluations, limitations, and future research directions.<\/jats:p>","DOI":"10.1145\/3614425","type":"journal-article","created":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T09:52:04Z","timestamp":1692438724000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Non-imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2468-9266","authenticated-orcid":false,"given":"Xiaodan","family":"Xing","sequence":"first","affiliation":[{"name":"Imperial College London, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7028-0363","authenticated-orcid":false,"given":"Huanjun","family":"Wu","sequence":"additional","affiliation":[{"name":"Imperial College London, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0426-5924","authenticated-orcid":false,"given":"Lichao","family":"Wang","sequence":"additional","affiliation":[{"name":"Imperial College London, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7848-4154","authenticated-orcid":false,"given":"Iain","family":"Stenson","sequence":"additional","affiliation":[{"name":"Alan Turing Institute, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0465-1623","authenticated-orcid":false,"given":"May","family":"Yong","sequence":"additional","affiliation":[{"name":"Alan Turing Institute, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1260-9775","authenticated-orcid":false,"given":"Javier","family":"Del Ser","sequence":"additional","affiliation":[{"name":"TECNALIA, Basque Research &amp; Technology Alliance (BRTA), Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0497-5297","authenticated-orcid":false,"given":"Simon","family":"Walsh","sequence":"additional","affiliation":[{"name":"Imperial College London, London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7344-7733","authenticated-orcid":false,"given":"Guang","family":"Yang","sequence":"additional","affiliation":[{"name":"Imperial College London, London, UK"}]}],"member":"320","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-25955-8_22"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.21057"},{"key":"e_1_3_1_5_2","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"214","volume":"70","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. 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