Clinical bottom line
AI-enhanced imaging modalities show promising potential for early detection of cardiac amyloidosis, a condition often diagnosed late due to nonspecific symptoms and subtle imaging findings. Integrating AI algorithms with multimodal imaging—including echocardiography, nuclear imaging, and cardiac MRI—can improve sensitivity and specificity, enabling earlier diagnosis and intervention. However, current evidence is largely exploratory, with validation gaps and challenges in clinical implementation. Careful integration of AI tools alongside physician expertise is essential to optimize diagnostic accuracy and patient outcomes (Abovich et al., 2026; Santarelli et al., 2021).
What the evidence shows
Cardiac amyloidosis is characterized by extracellular amyloid fibril deposition leading to restrictive cardiomyopathy. Early diagnosis is critical to improving prognosis but remains challenging due to overlapping clinical and imaging features with other cardiomyopathies (Gräni et al., 2024).
Santarelli et al. (2021) review conventional imaging techniques, highlighting their limitations in detecting early or subclinical disease. AI-enhanced imaging approaches leverage pattern recognition and quantitative analysis to identify subtle myocardial changes invisible to the human eye.
Abovich et al. (2026) demonstrate that multimodal AI models combining echocardiographic strain imaging, cardiac MRI tissue characterization, and nuclear scintigraphy data improve diagnostic performance for cardiac amyloidosis compared to single-modality assessment. These models also facilitate risk stratification by correlating imaging biomarkers with clinical outcomes.
Venturiello et al. (2026) discuss AI applications in electrocardiography as a complementary digital biomarker platform, potentially enabling noninvasive screening and monitoring.
Priya et al. (2026) emphasize the value of integrating clinical data with imaging features in AI algorithms to enhance early diagnosis and predict disease progression.
Fayez et al. (2024) stress the importance of physician awareness and routine imaging assessment to maximize the benefits of AI tools in clinical workflows.
Caveats and uncertainty
Most AI studies in cardiac amyloidosis are retrospective with limited sample sizes and lack prospective validation.
Heterogeneity in imaging protocols and AI model architectures hampers generalizability.
Interpretability and transparency of AI decision-making remain concerns for clinical adoption.
Integration into existing diagnostic pathways and electronic health records is not yet standardized.
Potential biases in training datasets may affect performance across diverse populations.
How this may change practice
AI-enhanced imaging could augment radiologists’ and cardiologists’ ability to detect cardiac amyloidosis earlier, enabling timely therapeutic interventions.
Routine use of AI tools may improve screening in at-risk populations, such as patients with unexplained heart failure or neuropathy.
Multimodal AI approaches may streamline diagnostic workflows by prioritizing cases for further evaluation.
Physician education on AI capabilities and limitations will be essential for effective implementation.
Future guidelines may incorporate AI-assisted imaging as part of standard diagnostic algorithms.