Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.

IF 3.7 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Paul Nona, Juma Bin Firos, Carrie Zografos, Mark J Schuuring, Ami Bhatt, Efstathia Andrikopoulou
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引用次数: 0

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.

智能技术,脆弱的心脏:导航人工智能在心力衰竭管理中的挑战和局限性。
综述目的:本综述综合了人工智能在心力衰竭治疗中的应用的最新进展,包括表型分析、风险分层、成像解释和护理点决策支持,并描述了目前限制安全和公平临床翻译的障碍。最近的发现:临床数据集仍然是异构和不完整的;电子记录、遥测和成像存储库之间的碎片化限制了通用性和外部有效性。关键亚组的代表性不足和结果分类错误会导致系统性误差,从而扩大差异。性能随着治疗方法和工作流程的发展而变化,但部署后的监控并不常见。模型不透明阻碍了错误分析和临床医生的信任。监管和数据共享框架正在不断发展和不一致,使多站点验证和持续监测复杂化。得到最有力支持的缓解战略包括严格的队列管理;透明的报告;地理和时间的外部验证;接受预先规定的安全检查的准飞行员;基于权益指标的偏见审计简洁的文件,如模型卡和说明书;持续监测,制定明确的应急和回滚计划;人类的监督贯穿于治理之中。在开发和实施中嵌入保障措施可以使人工智能在心力衰竭护理中提供可衡量的价值,同时保护患者安全和公平。当务之急是进行强有力的评估,对漂移和危害进行常规监测,并与对患者重要的结果保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Current Heart Failure Reports
Current Heart Failure Reports Medicine-Emergency Medicine
CiteScore
5.30
自引率
0.00%
发文量
44
期刊介绍: This journal intends to provide clear, insightful, balanced contributions by international experts that review the most important, recently published clinical findings related to the diagnosis, treatment, management, and prevention of heart failure. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas, such as investigative, pharmacologic, and nonpharmacologic therapies, pathophysiology, and prevention. Section Editors, in turn, select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An international Editorial Board reviews the annual table of contents, suggests articles of special interest to their country/region, and ensures that topics are current and include emerging research. Commentaries from well-known figures in the field are also provided.
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