Cheyenne S L Chiu, Willem Gerrits, Marco Guglielmo, Maarten J Cramer, Pim van der Harst, René van Es, Mathias Meine
{"title":"从临床到云端:人工智能辅助远程监测植入式心脏装置患者的疗效。","authors":"Cheyenne S L Chiu, Willem Gerrits, Marco Guglielmo, Maarten J Cramer, Pim van der Harst, René van Es, Mathias Meine","doi":"10.1111/pace.70036","DOIUrl":null,"url":null,"abstract":"<p><p>The integration of telehealth, particularly remote monitoring (RM), has profoundly improved the care of patients with cardiac implantable electronic devices (CIEDs). The recent COVID-19 pandemic has further accelerated the adoption of RM systems. The implementation of RM to standard clinical care has been accompanied by a surge of device transmissions. Especially unscheduled transmissions have resulted in an overwhelming workload for clinicians. As the number of device transmissions is expected to increase further while clinical resources remain limited, workflow optimization is crucial. Artificial intelligence (AI) presents a promising solution. This review outlines recent advances in RM and AI applications for CIEDs. It explores the potential of AI to streamline RM workflows, reduce clinician workload, and enhance heart failure care by enabling early detection of clinical deterioration and timely intervention. In addition, key barriers to implementation are addressed, including data standardization and regulatory considerations. Beyond improving monitoring efficiency and patient outcomes, AI-supported RM may also help expand access to care through more effective resource allocation and contribute to a more sustainable, future-proof healthcare system.</p>","PeriodicalId":520740,"journal":{"name":"Pacing and clinical electrophysiology : PACE","volume":" ","pages":"1106-1113"},"PeriodicalIF":1.3000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504922/pdf/","citationCount":"0","resultStr":"{\"title\":\"From Clinic to Cloud: Efficacy of AI-Assisted Remote Monitoring of Patients With Implantable Cardiac Devices.\",\"authors\":\"Cheyenne S L Chiu, Willem Gerrits, Marco Guglielmo, Maarten J Cramer, Pim van der Harst, René van Es, Mathias Meine\",\"doi\":\"10.1111/pace.70036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The integration of telehealth, particularly remote monitoring (RM), has profoundly improved the care of patients with cardiac implantable electronic devices (CIEDs). The recent COVID-19 pandemic has further accelerated the adoption of RM systems. The implementation of RM to standard clinical care has been accompanied by a surge of device transmissions. Especially unscheduled transmissions have resulted in an overwhelming workload for clinicians. As the number of device transmissions is expected to increase further while clinical resources remain limited, workflow optimization is crucial. Artificial intelligence (AI) presents a promising solution. This review outlines recent advances in RM and AI applications for CIEDs. It explores the potential of AI to streamline RM workflows, reduce clinician workload, and enhance heart failure care by enabling early detection of clinical deterioration and timely intervention. In addition, key barriers to implementation are addressed, including data standardization and regulatory considerations. Beyond improving monitoring efficiency and patient outcomes, AI-supported RM may also help expand access to care through more effective resource allocation and contribute to a more sustainable, future-proof healthcare system.</p>\",\"PeriodicalId\":520740,\"journal\":{\"name\":\"Pacing and clinical electrophysiology : PACE\",\"volume\":\" \",\"pages\":\"1106-1113\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504922/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pacing and clinical electrophysiology : PACE\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/pace.70036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/8/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pacing and clinical electrophysiology : PACE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/pace.70036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/21 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
From Clinic to Cloud: Efficacy of AI-Assisted Remote Monitoring of Patients With Implantable Cardiac Devices.
The integration of telehealth, particularly remote monitoring (RM), has profoundly improved the care of patients with cardiac implantable electronic devices (CIEDs). The recent COVID-19 pandemic has further accelerated the adoption of RM systems. The implementation of RM to standard clinical care has been accompanied by a surge of device transmissions. Especially unscheduled transmissions have resulted in an overwhelming workload for clinicians. As the number of device transmissions is expected to increase further while clinical resources remain limited, workflow optimization is crucial. Artificial intelligence (AI) presents a promising solution. This review outlines recent advances in RM and AI applications for CIEDs. It explores the potential of AI to streamline RM workflows, reduce clinician workload, and enhance heart failure care by enabling early detection of clinical deterioration and timely intervention. In addition, key barriers to implementation are addressed, including data standardization and regulatory considerations. Beyond improving monitoring efficiency and patient outcomes, AI-supported RM may also help expand access to care through more effective resource allocation and contribute to a more sustainable, future-proof healthcare system.