Heart Valve Centers in the Era of Data-Driven and AI-Enabled Valve Care
Abstract
Heart valve disease management has entered a phase of unprecedented clinical and organizational complexity. Beyond anatomical valve lesion severity, outcomes are increasingly influenced by myocardial damage, ventricular remodeling, comorbidities, and extracardiac involvement, which often evolve independently of symptoms. This growing dissociation between valve lesions, myocardial involvement, and clinical presentation challenges traditional symptom- and procedure-centered paradigms and contributes to delayed referral, heterogeneous practice patterns, and variable outcomes, despite major advances in surgical and transcatheter therapies. Concurrently, multimodality imaging, longitudinal data acquisition, and expanding therapeutic options have transformed valve care into a data-rich, lifetime-oriented discipline. In this context, artificial intelligence has emerged as a potential facilitator of automated image analysis, phenotypic characterization, risk stratification, and longitudinal monitoring. However, the clinical value of these technologies critically depends on their integration within structured systems capable of ensuring data quality, expert validation, and accountable decision-making. This article argues that contemporary valve care requires an organizational framework that aligns biological complexity with technological innovation and proposes an integrated model combining Heart Valve Clinics, multidisciplinary Heart Teams, institutional Heart Valve Centers of Reference, and coordinated Valve Networks. Within this framework, artificial intelligence supports, rather than replaces, expert clinical judgment by enhancing consistency, reducing unwarranted variability, and enabling more timely and individualized decision-making across the disease trajectory, with the ultimate aim of improving quality, efficiency, sustainability, and equity of care.
Related
- Neural Crest-Derived Endocardial Cells Required for Heart Valve Development
- Predictors of aging perception among community dwelling older adults attending primary care centers in Qazvin
- Functional Performance of Electrospun Tissue-Engineered Heart Valve Scaffolds Across Geometries Under Aortic Conditions
- HEMATOLOGICAL PROFILE OF WOMEN WITH RECURRENT PREGNANCY LOSS: A CROSS-SECTIONAL STUDY AT SPECIALIZED CARE CENTERS IN SALVADOR, BAHIA, BRAZIL
- Family and informal caregivers’ acceptance, concerns and expectations regarding artificial intelligence-enabled dementia care: a protocol for qualitative evidence synthesis
BibTeX
@article{lancellotti2026heart,
title = {{Heart Valve Centers in the Era of Data-Driven and AI-Enabled Valve Care}},
author = {Patrizio Lancellotti and Augustin Coisne and Raluca Dulgheru and Bernard Cosyns and Madalina Garbi and Geu-Ru Hong and Jadranka Separovic-Hanzevacki and Marco Moscarelli and Tadafumi Sugimoto and Erwan Donal and Khalil Fattouch and Gilbert Habib and Rodolfo Citro and Marie‐annick Clavel and Tommaso Viva and Cécile Oury and Mani Vannan and Philippe Pibarot},
journal = {Journal of the Heart Valve Society},
year = {2026},
month = oct,
doi = {10.1177/30494826261433538},
url = {https://hal.science/hal-05830220v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Heart Valve Centers in the Era of Data-Driven and AI-Enabled Valve Care
AU - Patrizio Lancellotti
AU - Augustin Coisne
AU - Raluca Dulgheru
AU - Bernard Cosyns
AU - Madalina Garbi
AU - Geu-Ru Hong
AU - Jadranka Separovic-Hanzevacki
AU - Marco Moscarelli
AU - Tadafumi Sugimoto
AU - Erwan Donal
AU - Khalil Fattouch
AU - Gilbert Habib
AU - Rodolfo Citro
AU - Marie‐annick Clavel
AU - Tommaso Viva
AU - Cécile Oury
AU - Mani Vannan
AU - Philippe Pibarot
PY - 2026
DA - 2026/10/21
JO - Journal of the Heart Valve Society
DO - 10.1177/30494826261433538
UR - https://hal.science/hal-05830220v1
AB - Heart valve disease management has entered a phase of unprecedented clinical and organizational complexity. Beyond anatomical valve lesion severity, outcomes are increasingly influenced by myocardial damage, ventricular remodeling, comorbidities, and extracardiac involvement, which often evolve independently of symptoms. This growing dissociation between valve lesions, myocardial involvement, and clinical presentation challenges traditional symptom- and procedure-centered paradigms and contributes to delayed referral, heterogeneous practice patterns, and variable outcomes, despite major advances in surgical and transcatheter therapies. Concurrently, multimodality imaging, longitudinal data acquisition, and expanding therapeutic options have transformed valve care into a data-rich, lifetime-oriented discipline. In this context, artificial intelligence has emerged as a potential facilitator of automated image analysis, phenotypic characterization, risk stratification, and longitudinal monitoring. However, the clinical value of these technologies critically depends on their integration within structured systems capable of ensuring data quality, expert validation, and accountable decision-making. This article argues that contemporary valve care requires an organizational framework that aligns biological complexity with technological innovation and proposes an integrated model combining Heart Valve Clinics, multidisciplinary Heart Teams, institutional Heart Valve Centers of Reference, and coordinated Valve Networks. Within this framework, artificial intelligence supports, rather than replaces, expert clinical judgment by enhancing consistency, reducing unwarranted variability, and enabling more timely and individualized decision-making across the disease trajectory, with the ultimate aim of improving quality, efficiency, sustainability, and equity of care.
SN - 3049-4826
ER -