Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability and generalization constraints.

The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation has traditionally relied on cardiologists' expertise. Deep learning has revolutionized medical data analysis, especially within ECG diagnostics. However, the challenge of inter-patient variability limits the generalizability of ECG-AI models trained on population datasets, often reducing accuracy for specific patients or groups. While prior studies have developed various deep-learning techniques to address this issue, these advancements largely focus on universal models without tailoring to individual patient needs. A systematic review methodology was employed, comprehensively searching four major databases (PubMed, IEEE Xplore, Web of Science, and Google Scholar), meticulously screening and analyzing studies from 2020 to 2024 using a rigorous two-step selection process to ensure methodological quality and relevance, ultimately yielding 112 studies for comprehensive analysis. This review offers a unique perspective by systematically examining recent deep-learning approaches designed explicitly for personalized ECG diagnosis, emphasizing models that address patient-specific variability. Using a rigorous methodology for selecting and analyzing relevant studies, we provide an in-depth overview of advanced techniques, including transfer learning, generative adversarial networks, meta-learning, and domain adaptation. The review also investigates the limitations of these methods, such as balancing generalization with patient specificity and addressing data privacy concerns. By identifying these challenges and outlining future directions, this review highlights the transformative potential of deep learning for ECG diag-nostics in clinical practice. Our findings underscore a pathway toward more accurate, efficient, and patient-centered cardiac diagnostics, setting a foundation for future personalized care innovations.