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Due to a lack of a causative therapy, an accurate prediction of women at risk for the disease and its associated adverse outcomes is of utmost importance to tailor care. In the past two decades, there have been successful improvements in screening as well as in the prediction of the disease in high-risk women. This is due to, among other things, the introduction of biomarkers such as the sFlt-1\/PlGF ratio. Recently, the traditional definition of preeclampsia has been expanded based on new insights into the pathophysiology and conclusive evidence on the ability of angiogenic biomarkers to improve detection of preeclampsia-associated maternal and fetal adverse events.<\/jats:p><jats:p>However, with the widespread availability of digital solutions, such as decision support algorithms and remote monitoring devices, a chance for a further improvement of care\u00a0arises. Two lines of research and application are promising: First, on the patient side, home monitoring has the potential to transform the traditional care pathway. The importance of the ability to input and access data remotely is a key learning from the COVID-19 pandemic. Second, on the physician side, machine-learning-based decision support algorithms have been shown to improve precision in clinical decision-making. The integration of signals from patient-side remote monitoring devices into predictive algorithms that power physician-side decision support tools offers a chance to further improve care.<\/jats:p><jats:p>The purpose of this review is to summarize the recent advances in prediction, diagnosis and monitoring of preeclampsia and its associated adverse outcomes. We will review the potential impact of the ability to access to clinical data via remote monitoring. In the combination of advanced, machine learning-based risk calculation and remote monitoring lies an unused potential that allows for a truly patient-centered care.<\/jats:p>","DOI":"10.1007\/s00404-022-06864-y","type":"journal-article","created":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T12:02:35Z","timestamp":1671969755000},"page":"1663-1677","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring"],"prefix":"10.1007","volume":"308","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-7028","authenticated-orcid":false,"given":"Max","family":"Hackel\u00f6er","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leon","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3507-8636","authenticated-orcid":false,"given":"Stefan","family":"Verlohren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,25]]},"reference":[{"issue":"9827","key":"6864_CR1","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1016\/S0140-6736(11)60785-X","volume":"379","author":"AH Shennan","year":"2012","unstructured":"Shennan AH, Redman C, Cooper C, Milne F (2012) Are most maternal deaths from pre-eclampsia avoidable? 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