{"id":"f2c66dbe15d9","type":"article","url":"https://hartvaat.nl/2026/03/26/kunstmatige-intelligentie-in-de-cardiovasculaire-geneeskunde-focus-op-hypertensi/","title":"Kunstmatige intelligentie in de cardiovasculaire geneeskunde: focus op hypertensie","title_en":"Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension","category":"atriumfibrilleren","category_label":"Atriumfibrilleren","professions":["cardioloog","huisarts","internist"],"tags":["acuut-hartfalen","biomarkers-cardiovasculair","bloeddrukbehandeling","diabetes-en-hart","endotheel","farmaco-economie","gepersonaliseerde-geneeskunde","laminopathie","menopauze","obesitas","precision-trial","slaapapneu","thuisbloeddrukmeting","vrouwen"],"journal":"Hypertension","doi":"https://www.ahajournals.org/doi/abs/10.1161/HYPERTENSIONAHA.126.26094","source_url":"https://doi.org/https://www.ahajournals.org/doi/abs/10.1161/HYPERTENSIONAHA.126.26094","authors":["Fahimeh Varzideh"],"significance":6,"published":"2026-03-26","source_date":"2026-03-26","image":"","kennis":["https://hartvaat.nl/kennis/atriumfibrilleren/sotalol-bij-af/","https://hartvaat.nl/kennis/atriumfibrilleren/chadsvasc-score/"],"congress":"","summary_en":"Hypertension remains the most prevalent modifiable risk factor worldwide, while blood-pressure control stays persistently suboptimal — partly because care models rely on episodic measurements and population-based algorithms. This systematic review maps how artificial intelligence (AI) can support the entire hypertension care continuum: risk prediction, phenotyping, blood-pressure measurement, wearable-based monitoring, trial analysis, population health, detection of secondary hypertension, behavioural and adherence interventions, and multi-omics-driven precision medicine. The authors stress the methodological prerequisites for clinically meaningful AI — robust ground-truth standards, external and temporal validation, interpretability, workflow integration and equity-aware design — and discuss cuffless BP measurement, natural language processing and decision support, with the ethical and regulatory challenges. AI could shift hypertension care from reactive and threshold-based to predictive, personalised and patient-centred, provided it is carefully validated and implemented.","created":"2026-07-03T10:32:20Z","updated":"2026-07-03T18:39:22Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Hypertensie blijft wereldwijd de meest voorkomende beïnvloedbare risicofactor, terwijl de bloeddrukcontrole hardnekkig suboptimaal blijft — deels door zorgmodellen die leunen op losse metingen en populatiegebaseerde algoritmen. Deze systematische review brengt in kaart hoe kunstmatige intelligentie (AI) de hele hypertensiezorg kan ondersteunen: risicovoorspelling, fenotypering, bloeddrukmeting, monitoring met wearables, analyse van trials, populatiegezondheid, detectie van secundaire hypertensie, gedrags- en therapietrouw-interventies en multi-omics-gedreven precisiegeneeskunde. De auteurs benadrukken de methodologische voorwaarden voor klinisch zinvolle AI — robuuste referentiestandaarden, externe en temporele validatie, interpreteerbaarheid, integratie in de workflow en aandacht voor gelijkheid — en bespreken cuffless bloeddrukmeting, natuurlijke-taalverwerking en beslissingsondersteuning, met de ethische en regelgevende uitdagingen. AI kan de hypertensiezorg verschuiven van reactief en drempelgebaseerd naar voorspellend, gepersonaliseerd en patiëntgericht, mits zorgvuldig gevalideerd en geïmplementeerd.","abstract_original":"Hypertension, Volume 83, Issue 6, Page e26094, June 1, 2026. Hypertension remains the most prevalent modifiable risk factor for cardiovascular morbidity and mortality worldwide, yet rates of effective blood pressure control remain persistently suboptimal despite the availability of multiple therapeutic options. This gap reflects fundamental limitations of current care models, which rely on episodic measurements, population-based treatment algorithms, and incomplete representation of the biological, behavioral, and social complexity underlying blood pressure regulation. Artificial intelligence (AI) offers a transformative framework to address these challenges by enabling the integration of longitudinal, multimodal data and modeling nonlinear, dynamic relationships that are difficult to capture with conventional approaches. This systematic review synthesizes emerging evidence on the application of AI across the hypertension care continuum, including risk prediction, phenotyping, blood pressure measurement, wearable-based monitoring, clinical trial analysis, population health modeling, detection of secondary hypertension, behavioral and adherence interventions, and multi-omics–driven precision medicine. We highlight the methodological foundations required for clinically meaningful AI, emphasizing robust ground-truth definitions, external and temporal validation, interpretability, workflow integration, and equity-aware design. The review also examines the promise and limitations of natural language processing, cuffless blood pressure technologies, and AI-guided decision support systems, alongside ethical, regulatory, and implementation challenges. Collectively, current evidence suggests that AI has the potential to shift hypertension management from a reactive, threshold-based paradigm toward a more predictive, personalized, and patient-centered model. Realizing this potential will depend on rigorous validation, thoughtful implementation, and sustained alignment with clinical, ethical, and equity principles."}