AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Cardiovascular diseases remain the leading cause of mortality worldwide, accounting for 18 million deaths annually. Detection and prediction of cardiovascular conditions are essential for timely intervention and improved patient outcomes. Wearable devices offer a promising, noninvasive solution for continuous monitoring of cardiovascular signals, vital signs, and physical activity. However, the large data volumes generated by these devices and the rapid fluctuations in cardiovascular signals necessitate advanced artificial intelligence (AI) techniques for real-time analysis and effective clinical decision-making. OBJECTIVE: The objective of this scoping review was to identify the main challenges of AI-driven platforms for real-time cardiovascular condition monitoring with wearable devices and explore potential solutions. In addition, this review aimed to examine how AI algorithms are developed for robust monitoring and how deployment pipelines are optimized to enable real-time cardiovascular condition monitoring. METHODS: A comprehensive search was conducted in the following electronic databases: MEDLINE(R) ALL (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (Ovid), Web of Science Core Collection (Clarivate), IEEE Xplore, and ACM Digital Library, yielding 2385 unique records. Inclusion criteria focused on studies that used wearable devices for participant data collection and applied AI algorithms for real-time analysis to detect or predict cardiovascular events and diseases. After title and abstract screening, 153 papers remained, and following a full-text review, 19 studies met the inclusion criteria. RESULTS: The findings indicate that despite the promise of AI and wearable devices, research on real-time cardiovascular monitoring remains limited and lacks comprehensive validation. Most studies relied on publicly available wearable datasets rather than real-world validation with recruited participants in community settings. Studies that deployed AI algorithms in real time frequently failed to report operational characteristics and challenges. Electrocardiography-based wearable sensors were the most frequently used devices, primarily in hospital settings. A variety of AI techniques, ranging from traditional machine learning to lightweight deep learning algorithms, were deployed either on wearable devices or via cloud-based processing. CONCLUSIONS: Robust, interdisciplinary research is needed to harness the full potential of AI-driven, real-time cardiovascular health management using wearable devices. This includes the development and validation of scalable solutions for continuous community-based deployment. Furthermore, real-world challenges such as participant compliance, hardware and connectivity constraints, and AI model optimization for real-time continuous monitoring must be carefully addressed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».