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The editorial frames heart failure as a major global cardiovascular challenge and emphasizes how data-driven tools can enable personalized, proactive care while reducing decompensation risk, hospitalizations, and burdens on healthcare systems. 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\nEDITED AND REVIEWED BY  \nKiyotake Ishikawa,  \nIcahn School of Medicine at Mount Sinai, United States  \n*CORRESPONDENCE  \nAndre Rodrigues Duraes  \n [andreduraes@gmail.com](andreduraes@gmail.com)  \nRECEIVED 14 April 2025  \nACCEPTED 26 May 2025  \nPUBLISHED 03 June 2025  \nCITATION  \nDuraes AR, Gomes-Neto M and Bocchi EA (2025) Editorial: Contemporary applications of machine learning and artificial intelligence for the management of heart failure.  \nFront. Cardiovasc. Med. 12:1611859 .  \ndoi: 10.3389/fcvm.2025.1611859  \nCOPYRIGHT  \n© 2025 Duraes, Gomes-Neto and Bocchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEditorial: Contemporary applications of machine learning and artificial intelligence for the management of heart failure  \nAndre Rodrigues Duraes1*, Mansueto Gomes-Neto1 and Edimar Alcides Bocchi2  \n1Federal University of Bahia (UFBA), Salvador, Brazil, 2Heart Institute (InCor), São Paulo, Sao Paulo, Brazil  \nHeart failure (HF) is a complex syndrome with substantial clinical and economic impact. This editorial highlights four original articles published in Frontiers in Cardiovascular Medicine that showcase contemporary applications of machine learning and artificial intelligence (AI) in HF management. These studies address early diagnosis through novel biomarkers, disease stratification based on transcriptomics, mechanistic insight into apoptotic pathways, and predictive telemonitoring using real-time AI models. Collectively, these contributions exemplify the transformative potential of data-driven technologies in personalizing care and preventing decompensation in HF. We discuss both the promise and challenges of integrating these tools into routine.  \nKEYWORDS  \nheart failure, machine learning, artificial intelligence, biomarkers, precision medicine, telemonitoring, cardiomyopathy, healthcare technology  \nEditorial on the Research Topic  \nContemporary applications of machine learning and artificial intelligence for the management of heart failure  \nHeart failure (HF) remains one of the top challenges in cardiovascular medicine worldwide. It’s associated with high morbidity, frequent hospitalizations, and a growing economic burden on healthcare systems. Recent advancements in precision medicine and artificial intelligence (AI) offer exciting new avenues for understanding, diagnosing, and managing HF—especially in early or pre-symptomatic stages. This issue of Frontiers in Cardiovascular Medicine brings together four original studies that shine light on these frontiers, from genetic biomarkers to predictive AI tools. Together, they exemplify the contemporary applications of machine learning and artificial intelligence for the management of heart failure—now emerging as key allies in advancing personalized and proactive cardiovascular care.  \nYu et al. used a strong bioinformatics and machine learning approach to identify four key genes—SMOC2, OGN, FCN3, and SERPINA3—that may serve as early markers for acute decompensated heart failure (ADHF) triggered by venous congestion. Often, venous congestion sets in well before symptoms are visible. These genes showed excellent diagnostic performance (AUCs > 0.9), and the study offers a well-validated diagnostic model that could help catch worsening HF earlier. In the near future, and  \nFrontiers in Cardiovascular Medicine 01 [frontiersin.org](frontiersin.org)  \nwith the emergence of more research evaluating this complex mechanism, patients with heart failure—a condition affecting over 64 million people worldwide and as","cbCaiqBsdtWCKzCc","https://ap.wps.com/l/cbCaiqBsdtWCKzCc","pdf",82512,"English","# Editorial on the Research Topic\n## Contemporary applications of machine learning and artificial intelligence for the management of heart failure\n## Machine learning and AI approaches in four featured studies\n## Editorial discussion: promise and challenges for routine integration","[{\"question\":\"What four areas do the featured studies address in heart failure management?\",\"answer\":\"They cover early diagnosis using novel biomarkers, transcriptomics-based disease stratification, mechanistic insight into apoptotic pathways, and predictive telemonitoring with real-time AI models.\"},{\"question\":\"How can AI help with earlier detection of acute decompensated heart failure?\",\"answer\":\"A study uses machine learning and bioinformatics to identify specific genes as early markers and provides a diagnostic model with strong performance, supporting earlier recognition before visible symptoms.\"},{\"question\":\"What is the role of remote AI telemonitoring in preventing hospitalizations?\",\"answer\":\"An AI model trained on TIM-HF2 trial data predicts HF-related hospitalizations within seven days and can outperform rule-based systems, enabling targeted monitoring of patients at highest predicted risk.\"}]","Editorial: Contemporary applications of machine learning and artificial intelligence for the management of heart failure | PDF"]