TY - JOUR
T1 - Artificial Intelligence Applications in Cardio-Oncology
T2 - A Comprehensive Review
AU - Guha, Avirup
AU - Shah, Viraj
AU - Nahle, Tarek
AU - Singh, Shivam
AU - Kunhiraman, Harikrishnan Hyma
AU - Shehnaz, Fathima
AU - Nain, Priyanshu
AU - Makram, Omar M.
AU - Mahmoudi, Morteza
AU - Al-Kindi, Sadeer
AU - Madabhushi, Anant
AU - Shiradkar, Rakesh
AU - Daoud, Hisham
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
PY - 2025/12
Y1 - 2025/12
N2 - Purpose of Review: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns. Recent Findings: AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. Summary: AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.
AB - Purpose of Review: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns. Recent Findings: AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. Summary: AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.
KW - Artificial intelligence
KW - Cardio-oncology
KW - Cardiotoxicity
KW - Deep learning
KW - Machine learning
KW - Personalized medicine
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U2 - 10.1007/s11886-025-02215-w
DO - 10.1007/s11886-025-02215-w
M3 - Article
C2 - 39969610
AN - SCOPUS:85219151161
SN - 1523-3782
VL - 27
JO - Current Cardiology Reports
JF - Current Cardiology Reports
IS - 1
M1 - 56
ER -