Scholarlink

Purpose of Review <p>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.</p> Recent Findings <p>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.</p> Summary <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review

  • 期刊论文

  • 发表日期：

    2025年2月19日

查看全文
  • Avirup Guha,
  • Viraj Shah,
  • Tarek Nahle,
  • Shivam Singh,
  • Harikrishnan Hyma Kunhiraman,
  • Fathima Shehnaz,
  • Priyanshu Nain,
  • Omar M. Makram,
  • Morteza Mahmoudi,
  • Sadeer Al-Kindi,
  • Anant Madabhushi,
  • Rakesh Shiradkar,
  • Hisham Daoud

摘要

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.