{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T21:06:53Z","timestamp":1784322413766,"version":"3.55.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Herz"],"DOI":"10.1007\/s00059-026-05389-3","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T20:07:57Z","timestamp":1784318877000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical applications of artificial intelligence in hypertension management: current evidence and future perspectives","Klinische Anwendungen k\u00fcnstlicher Intelligenz im Hypertoniemanagement: Aktuelle Evidenz und Zukunftsperspektiven"],"prefix":"10.1007","author":[{"given":"Ehsan","family":"Shahverdi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amin","family":"Shahverdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shadi","family":"Kamali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars Roman","family":"Herda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"5389_CR1","volume-title":"Hypertension","author":"World Health Organization","year":"2021","unstructured":"World Health Organization (2021) Hypertension. WHO, Geneva"},{"key":"5389_CR2","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1016\/S0140-6736(20)30752-2","volume":"396","author":"CJL Murray","year":"2020","unstructured":"GBD 2019 Risk Factors Collaborators, Murray\u00a0CJL, Aravkin\u00a0AY et\u00a0al (2020) Global burden of 87 risk factors in 204 countries and territories, 1990\u20132019. Lancet 396:1223\u20131249","journal-title":"Lancet"},{"key":"5389_CR3","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/S0140-6736(21)01330-1","volume":"398","author":"B Zhou","year":"2021","unstructured":"NCD Risk Factor Collaboration (NCD-RisC), Zhou\u00a0B, Carrillo-Larco\u00a0RM et\u00a0al (2021) Worldwide trends in hypertension prevalence and progress in treatment and control. Lancet 398:957\u2013980","journal-title":"Lancet"},{"key":"5389_CR4","first-page":"e13","volume":"71","author":"PK Whelton","year":"2018","unstructured":"Whelton\u00a0PK, Carey\u00a0RM, Aronow\u00a0WS et\u00a0al (2018) 2017 ACC\/AHA guideline for the prevention, detection, evaluation, and management of high blood pressure. Hypertension 71:e13\u2013115","journal-title":"Hypertension"},{"key":"5389_CR5","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.1093\/eurheartj\/ehy339","volume":"39","author":"B Williams","year":"2018","unstructured":"Williams\u00a0B, Mancia\u00a0G, Spiering\u00a0W et\u00a0al (2018) 2018 ESC\/ESH guidelines for the management of arterial hypertension. Eur Heart J 39:3021\u20133104","journal-title":"Eur Heart J"},{"key":"5389_CR6","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","volume":"117","author":"RB D\u2019Agostino Sr","year":"2008","unstructured":"D\u2019Agostino\u00a0RB Sr, Vasan\u00a0RS, Pencina\u00a0MJ et\u00a0al (2008) General cardiovascular risk profile for use in primary care. Circulation 117:743\u2013753","journal-title":"Circulation"},{"key":"5389_CR7","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol\u00a0EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44\u201356","journal-title":"Nat Med"},{"key":"5389_CR8","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","volume":"375","author":"Z Obermeyer","year":"2016","unstructured":"Obermeyer\u00a0Z, Emanuel\u00a0EJ (2016) Predicting the future\u2014big data, machine learning, and clinical medicine. N Engl J Med 375:1216\u20131219","journal-title":"N Engl J Med"},{"key":"5389_CR9","first-page":"1","volume":"69","author":"S Saria","year":"2018","unstructured":"Saria\u00a0S, Butte\u00a0A, Sheikh\u00a0A (2018) Better medicine through machine learning: what\u2019s real, and what\u2019s artificial? Annu Rev Med 69:1\u201315","journal-title":"Annu Rev Med"},{"key":"5389_CR10","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1093\/eurheartj\/ehz056","volume":"40","author":"C Krittanawong","year":"2019","unstructured":"Krittanawong\u00a0C, Johnson\u00a0KW, Rosenson\u00a0RS et\u00a0al (2019) Machine learning prediction in cardiovascular diseases. Eur Heart J 40:205\u2013212","journal-title":"Eur Heart J"},{"key":"5389_CR11","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1038\/s41569-021-00528-1","volume":"18","author":"F Wang","year":"2021","unstructured":"Wang\u00a0F, Preininger\u00a0A, Zhang\u00a0Y et\u00a0al (2021) Clinical artificial intelligence applications in cardiovascular disease. Nat Rev Cardiol 18:313\u2013330","journal-title":"Nat Rev Cardiol"},{"key":"5389_CR12","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1016\/S0140-6736(18)30309-X","volume":"391","author":"RJ McManus","year":"2018","unstructured":"McManus\u00a0RJ, Mant\u00a0J, Haque\u00a0MS et\u00a0al (2018) Telemonitoring and self-management in hypertension. Lancet 391:949\u2013959","journal-title":"Lancet"},{"key":"5389_CR13","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp2005835","volume":"382","author":"S Keesara","year":"2020","unstructured":"Keesara\u00a0S, Jonas\u00a0A, Schulman\u00a0K (2020) COVID-19 and health care\u2019s digital revolution. N Engl J Med 382:e82","journal-title":"N Engl J Med"},{"key":"5389_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly\u00a0CJ, Karthikesalingam\u00a0A, Suleyman\u00a0M et\u00a0al (2019) Key challenges for delivering clinical impact with AI. BMC Med 17:195","journal-title":"BMC Med"},{"key":"5389_CR15","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n160","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page\u00a0MJ, McKenzie\u00a0JE, Bossuyt\u00a0PM et\u00a0al (2021) PRISMA 2020 explanation and elaboration. BMJ 372:n160","journal-title":"BMJ"},{"key":"5389_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1000097","volume":"6","author":"D Moher","year":"2009","unstructured":"Moher\u00a0D, Liberati\u00a0A, Tetzlaff\u00a0J et\u00a0al (2009) Preferred reporting items for systematic reviews and meta-analyses. PLoS Med 6:e1000097","journal-title":"PLoS Med"},{"key":"5389_CR17","doi-asserted-by":"publisher","first-page":"735","DOI":"10.7326\/L15-5093-2","volume":"162","author":"GS Collins","year":"2015","unstructured":"Collins\u00a0GS, Reitsma\u00a0JB, Altman\u00a0DG et\u00a0al (2015) Transparent reporting of multivariable prediction models (TRIPOD). Ann Intern Med 162:735\u2013736","journal-title":"Ann Intern Med"},{"key":"5389_CR18","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff\u00a0RF, Moons\u00a0KGM, Riley\u00a0RD et\u00a0al (2019) PROBAST: a tool to assess risk of bias in prediction models. Ann Intern Med 170:51\u201358","journal-title":"Ann Intern Med"},{"key":"5389_CR19","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page\u00a0MJ, Moher\u00a0D, Bossuyt\u00a0PM et\u00a0al (2021) PRISMA 2020 statement: updated guideline. BMJ 372:n71","journal-title":"BMJ"},{"key":"5389_CR20","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.9268","volume":"20","author":"C Ye","year":"2018","unstructured":"Ye\u00a0C, Fu\u00a0T, Hao\u00a0S et\u00a0al (2018) Prediction of incident hypertension within the next year using electronic health records and machine learning. J\u00a0Med Internet Res 20:e22","journal-title":"J Med Internet Res"},{"key":"5389_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooac097","volume":"5","author":"S Datta","year":"2022","unstructured":"Datta\u00a0S, Morassi Sasso\u00a0A, Kiwit\u00a0N et\u00a0al (2022) Predicting hypertension onset from longitudinal electronic health records with deep learning. JAMIA Open 5:ooac097","journal-title":"JAMIA Open"},{"key":"5389_CR22","doi-asserted-by":"publisher","DOI":"10.2196\/52794","volume":"26","author":"SH Hwang","year":"2024","unstructured":"Hwang\u00a0SH, Lee\u00a0H, Lee\u00a0JH et\u00a0al (2024) Machine learning-based prediction for incident hypertension using nationwide cohorts. J\u00a0Med Internet Res 26:e52794","journal-title":"J Med Internet Res"},{"key":"5389_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18115838","volume":"18","author":"M Sharma","year":"2021","unstructured":"Sharma\u00a0M, Rajput\u00a0JS, Acharya\u00a0UR (2021) Automated detection of hypertension using physiological signals: a systematic review. Int J Environ Res Public Health 18:5838","journal-title":"Int J Environ Res Public Health"},{"key":"5389_CR24","doi-asserted-by":"publisher","first-page":"4216","DOI":"10.1109\/JBHI.2023.3278168","volume":"27","author":"ZD Liu","year":"2023","unstructured":"Liu\u00a0ZD, Li\u00a0Y, Zhang\u00a0YT et\u00a0al (2023) Cuffless blood pressure measurement using smart devices: validation study. IEEE J Biomed Health Inform 27:4216\u20134227","journal-title":"IEEE J Biomed Health Inform"},{"key":"5389_CR25","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1161\/HYPERTENSIONAHA.121.17747","volume":"78","author":"R Mukkamala","year":"2021","unstructured":"Mukkamala\u00a0R, Yavarimanesh\u00a0M, Natarajan\u00a0K et\u00a0al (2021) Evaluation of cuffless blood pressure measurement devices. Hypertension 78:1161\u20131171","journal-title":"Hypertension"},{"key":"5389_CR26","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1146\/annurev-bioeng-110220-014644","volume":"24","author":"R Mukkamala","year":"2022","unstructured":"Mukkamala\u00a0R, Stergiou\u00a0GS, Avolio\u00a0AP (2022) Cuffless blood pressure measurement. Annu Rev Biomed Eng 24:203\u2013230","journal-title":"Annu Rev Biomed Eng"},{"key":"5389_CR27","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1038\/s41371-022-00794-7","volume":"37","author":"EC Murray","year":"2023","unstructured":"Murray\u00a0EC, Delles\u00a0C, Orzechowski\u00a0P et\u00a0al (2023) Vascular phenotypes in early hypertension. J\u00a0Hum Hypertens 37:898\u2013906","journal-title":"J Hum Hypertens"},{"key":"5389_CR28","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/ehjdh\/ztad058","volume":"5","author":"C Li","year":"2024","unstructured":"Li\u00a0C, Liu\u00a0X, Shen\u00a0P et\u00a0al (2024) Machine learning modelling of electronic health records for cardiovascular risk prediction. Eur Heart J Digit Health 5:30\u201340","journal-title":"Eur Heart J Digit Health"},{"key":"5389_CR29","first-page":"1351","volume":"109","author":"J You","year":"2023","unstructured":"You\u00a0J, Yang\u00a0S, Qin\u00a0X et\u00a0al (2023) Machine learning-based models for cardiovascular risk prediction. Heart 109:1351\u20131359","journal-title":"Heart"},{"key":"5389_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.106018","volume":"30","author":"G Ntaios","year":"2021","unstructured":"Ntaios\u00a0G, Sagris\u00a0D, Kallipolitis\u00a0A et\u00a0al (2021) Machine-learning-derived cardiovascular risk stratification. J\u00a0Stroke Cerebrovasc Dis 30:106018","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"5389_CR31","first-page":"1660","volume":"34","author":"J Yi","year":"2024","unstructured":"Yi\u00a0J, Wang\u00a0L, Song\u00a0J et\u00a0al (2024) Predicting individual response to antihypertensive therapy using machine learning. Nutr Metab Cardiovasc Dis 34:1660\u20131669","journal-title":"Nutr Metab Cardiovasc Dis"},{"key":"5389_CR32","volume":"13","author":"H Hae","year":"2023","unstructured":"Hae\u00a0H, Kim\u00a0Y, Kim\u00a0J et\u00a0al (2023) Machine learning prediction of post-treatment blood pressure. J\u00a0Pers Med 13:786","journal-title":"J Pers Med"},{"key":"5389_CR33","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1007\/s11606-024-09068-z","volume":"40","author":"S Lin","year":"2025","unstructured":"Lin\u00a0S, Zhang\u00a0Z, Xie\u00a0F et\u00a0al (2025) Predictors of resistant hypertension using electronic health records. J\u00a0Gen Intern Med 40:1265\u20131274","journal-title":"J Gen Intern Med"},{"key":"5389_CR34","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1111\/jch.14791","volume":"26","author":"G Bakris","year":"2024","unstructured":"Bakris\u00a0G, Lin\u00a0PP, Xu\u00a0C et\u00a0al (2024) Machine learning prediction of cardiovascular risk in resistant hypertension. J\u00a0Clin Hypertens 26:500\u2013513","journal-title":"J Clin Hypertens"},{"key":"5389_CR35","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-079143","volume":"386","author":"J Song","year":"2024","unstructured":"Song\u00a0J, Wang\u00a0Y, Ahuja\u00a0S et\u00a0al (2024) AI-supported decision systems for hypertension management: randomized trial. BMJ 386:e079143","journal-title":"BMJ"},{"key":"5389_CR36","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1001\/jamainternmed.2023.8315","volume":"184","author":"L Samal","year":"2024","unstructured":"Samal\u00a0L, Bress\u00a0AP, McCoy\u00a0RG et\u00a0al (2024) Clinical decision support for hypertension: randomized trial. JAMA Intern Med 184:391\u2013400","journal-title":"JAMA Intern Med"},{"key":"5389_CR37","first-page":"907","volume":"319","author":"RJ McManus","year":"2018","unstructured":"McManus\u00a0RJ, Mant\u00a0J, Haque\u00a0MS et\u00a0al (2018) Self-monitoring and telemonitoring in hypertension management. JAMA 319:907\u2013917","journal-title":"JAMA"},{"key":"5389_CR38","doi-asserted-by":"publisher","DOI":"10.2196\/51916","volume":"8","author":"J Leitner","year":"2024","unstructured":"Leitner\u00a0J, Chiang\u00a0PH, Agnihotri\u00a0P et\u00a0al (2024) AI-based digital health intervention for blood pressure control. JMIR Cardio 8:e51916","journal-title":"JMIR Cardio"},{"key":"5389_CR39","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s41440-025-02444-0","volume":"49","author":"A Matsumoto","year":"2026","unstructured":"Matsumoto\u00a0A, Mineharu\u00a0Y, Kohjitani\u00a0H et\u00a0al (2026) Machine learning prediction in home BP monitoring adherence. Hypertens Res 49:852\u2013862","journal-title":"Hypertens Res"},{"key":"5389_CR40","doi-asserted-by":"publisher","DOI":"10.2196\/75551","volume":"27","author":"AS Moosa","year":"2025","unstructured":"Moosa\u00a0AS, Seng\u00a0JJB, Ng\u00a0CJ (2025) Clinical decision support systems using home BP data: scoping review. J\u00a0Med Internet Res 27:e75551","journal-title":"J Med Internet Res"},{"key":"5389_CR41","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.jacasi.2021.07.005","volume":"1","author":"A Cai","year":"2021","unstructured":"Cai\u00a0A, Zhu\u00a0Y, Clarkson\u00a0SA et\u00a0al (2021) Machine learning in hypertension and heart failure. JACC Asia 1:162\u2013172","journal-title":"JACC Asia"},{"key":"5389_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/s40885-024-00266-9","volume":"30","author":"JS Cho","year":"2024","unstructured":"Cho\u00a0JS, Park\u00a0JH (2024) Artificial intelligence in hypertension. Clin Hypertens 30:11","journal-title":"Clin Hypertens"},{"key":"5389_CR43","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1093\/ajh\/hpaf035","volume":"38","author":"A Karnik","year":"2025","unstructured":"Karnik\u00a0A, Yang\u00a0E (2025) Artificial intelligence to improve blood pressure control. Am J Hypertens 38:519\u2013526","journal-title":"Am J Hypertens"},{"key":"5389_CR44","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1097\/MBP.0000000000000711","volume":"29","author":"A Juyal","year":"2024","unstructured":"Juyal\u00a0A, Bisht\u00a0SB, Singh\u00a0MF (2024) AI and wearable technologies in hypertension. Blood Press Monit 29:260\u2013271","journal-title":"Blood Press Monit"},{"key":"5389_CR45","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly\u00a0CJ, Karthikesalingam\u00a0A, Suleyman\u00a0M et\u00a0al (2019) Challenges for clinical AI implementation. BMC Med 17:195","journal-title":"BMC Med"},{"key":"5389_CR46","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-023-00833-8","volume":"6","author":"WG La Cava","year":"2023","unstructured":"La Cava\u00a0WG, Saitta\u00a0S, Ahuja\u00a0S et\u00a0al (2023) Interpretable machine learning for clinical prediction. NPJ Digit Med 6:106","journal-title":"NPJ Digit Med"},{"key":"5389_CR47","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","volume":"7","author":"RJ Chen","year":"2023","unstructured":"Chen\u00a0RJ, Wang\u00a0JJ, Williamson\u00a0DFK et\u00a0al (2023) Algorithmic fairness in healthcare AI. Nat Biomed Eng 7:719\u2013742","journal-title":"Nat Biomed Eng"},{"key":"5389_CR48","first-page":"1115","volume":"31","author":"F Chen","year":"2024","unstructured":"Chen\u00a0F, Oh\u00a0A, B\u00e4rnighausen\u00a0T (2024) Bias detection in EHR-based AI models. J\u00a0Am Med Inform Assoc 31:1115\u20131125","journal-title":"J Am Med Inform Assoc"},{"key":"5389_CR49","first-page":"1365","volume":"31","author":"O Perets","year":"2024","unstructured":"Perets\u00a0O, Finkelstein\u00a0J, Buckeridge\u00a0DL et\u00a0al (2024) Bias in electronic health records: scoping review. J\u00a0Am Med Inform Assoc 31:1365\u20131376","journal-title":"J Am Med Inform Assoc"},{"key":"5389_CR50","doi-asserted-by":"publisher","first-page":"2929","DOI":"10.1038\/s41591-023-02608-w","volume":"29","author":"A Arora","year":"2023","unstructured":"Arora\u00a0A, Alderman\u00a0JE, Palmer\u00a0J et\u00a0al (2023) Standards for AI health datasets. Nat Med 29:2929\u20132938","journal-title":"Nat Med"},{"key":"5389_CR51","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01270-x","volume":"7","author":"V Muralidharan","year":"2024","unstructured":"Muralidharan\u00a0V, Adewale\u00a0BA, Huang\u00a0CJ et\u00a0al (2024) Reporting gaps in FDA-approved AI medical devices. NPJ Digit Med 7:273","journal-title":"NPJ Digit Med"},{"key":"5389_CR52","first-page":"266","volume":"36","author":"A Katirai","year":"2023","unstructured":"Katirai\u00a0A, Shen\u00a0N, Cockerill\u00a0R et\u00a0al (2023) Ethics of artificial intelligence in healthcare. Healthc Manage Forum 36:266\u2013270","journal-title":"Healthc Manage Forum"},{"key":"5389_CR53","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1161\/HYPERTENSIONAHA.124.22095","volume":"82","author":"D Shimbo","year":"2025","unstructured":"Shimbo\u00a0D, Shah\u00a0RU, Abdalla\u00a0M et\u00a0al (2025) AI in hypertension: NHLBI workshop report. Hypertension 82:36\u201345","journal-title":"Hypertension"}],"container-title":["Herz"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00059-026-05389-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00059-026-05389-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00059-026-05389-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T21:02:06Z","timestamp":1784322126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00059-026-05389-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"references-count":53,"alternative-id":["5389"],"URL":"https:\/\/doi.org\/10.1007\/s00059-026-05389-3","relation":{},"ISSN":["0340-9937","1615-6692"],"issn-type":[{"value":"0340-9937","type":"print"},{"value":"1615-6692","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,17]]},"assertion":[{"value":"5 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable. This study is a\u00a0narrative review and does not involve human participants or animal subjects.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"E.\u00a0Shahverdi, A.\u00a0Shahverdi, S.\u00a0Kamali and L.R.\u00a0Herda declare that they have no competing interests.","order":3,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}