Curr Heart Fail Rep. 2026 Jun 15;23(1):27. doi: 10.1007/s11897-026-00766-9.
ABSTRACT
PURPOSE OF REVIEW: This review synthesizes recent progress in applications of artificial intelligence to heart failure care, including phenotyping, risk stratification, imaging interpretation, and point-of-care decision support, and delineates barriers that currently limit safe and equitable clinical translation.
RECENT FINDINGS: Clinical datasets remain heterogeneous and incomplete; fragmentation across electronic records, telemetry, and imaging repositories constrains generalizability and external validity. Underrepresentation of key subgroups and outcome misclassification introduce systematic error that can widen disparities. Performance drifts as therapies and workflows evolve, yet monitoring after deployment is uncommon. Model opacity hinders error analysis and clinician trust. Regulatory and data-sharing frameworks are evolving and inconsistent, complicating multisite validation and ongoing surveillance. Mitigation strategies with the strongest support include rigorous cohort curation; transparent reporting; geographic and temporal external validation; prospective pilots with prespecified safety checks; bias auditing with equity metrics; concise documentation such as model cards and factsheets; continuous monitoring with clear contingency and rollback plans; and human oversight embedded throughout governance. Embedding safeguards into development and implementation can enable AI to deliver measurable value in heart failure care while protecting patient safety and equity. Immediate priorities are robust evaluation, routine surveillance for drift and harm, and alignment with outcomes that matter to patients.
PMID:42295460 | DOI:10.1007/s11897-026-00766-9