Clinical bottom line
AI-enhanced cardiac imaging is an emerging tool for early detection of radiation-induced cardiac injury (RICI), a significant late complication in patients undergoing thoracic radiotherapy. Advanced machine learning algorithms applied to cardiac MRI and multimodal imaging improve sensitivity in identifying subclinical myocardial changes before overt dysfunction develops. While promising, current evidence is largely preclinical or early clinical, necessitating further validation before routine clinical adoption. Integration of AI-driven imaging biomarkers with clinical data may enable personalized risk stratification and timely intervention to mitigate cardiac morbidity (Zhang et al., 2018; An et al., 2024; Priya et al., 2026).
What the evidence shows
Radiation-induced cardiac injury manifests as myocardial fibrosis, microvascular damage, and functional impairment, often detected late by conventional imaging. Zhang et al. (2018) provide a comprehensive review of advanced cardiac imaging modalities—including cardiac MRI, echocardiography, and nuclear techniques—highlighting their roles in detecting RICI.
Recent studies leverage AI and machine learning to enhance image analysis. An et al. (2024) demonstrated that advanced machine learning applied to cardiac MRI in preclinical genetic models identified early markers of radiation-induced cardiotoxicity with higher accuracy than traditional assessment.
Priya et al. (2026) describe AI-powered risk prediction models integrating multi-modal imaging and clinical data, improving early diagnosis and prognostication of cardiac injury post-radiotherapy.
Although AI applications in radiation-induced lung injury imaging have progressed (Wang et al., 2025), translation to cardiac imaging remains nascent but conceptually similar.
These AI-enhanced approaches enable detection of subtle tissue changes such as fibrosis and perfusion abnormalities, potentially before clinical symptoms or echocardiographic dysfunction appear.
Caveats and uncertainty
Most AI-enhanced cardiac imaging studies in RICI are preclinical or retrospective with small sample sizes.
Heterogeneity in imaging protocols and AI algorithms limits comparability and generalizability.
Clinical validation in diverse patient populations and prospective trials are lacking.
Integration into clinical workflows requires addressing data standardization, interpretability, and regulatory considerations.
Cost-effectiveness and impact on patient outcomes remain to be established.
How this may change practice
AI-enhanced cardiac imaging could transform surveillance of patients receiving thoracic radiotherapy by enabling earlier detection of subclinical injury.
Personalized risk stratification may guide cardioprotective interventions and tailored radiotherapy planning.
Radiologists and cardiologists will need to collaborate closely with data scientists to implement and interpret AI tools.
Routine use may improve long-term cardiac outcomes by facilitating timely management before irreversible damage.
Ongoing research should focus on prospective validation, standardization, and integration of AI imaging biomarkers into clinical decision-making.