Enhancing Trust in AI-Driven Diagnosis: A Novel Framework for Explainable Deep Learning in Early Detection of Cardiovascular Disease
Busayo Leah Ayodele, Leila Gheisi, Samson Ademola Adeniyi · International Journal of Research Publication and Reviews · 2024
The adoption of Artificial Intelligence (AI) in medical diagnostics has significantly advanced the early detection and treatment of cardiovascular disease (CVD), one of the leading causes of global mortality.Despite its proven accuracy, a critical barrier to widespread adoption remains the lack of trust due to the perceived opacity of AI-driven decision-making.This paper proposes a novel framework for Explainable Deep Learning (XDL) tailored to address this challenge by enhancing transparency and interpretability in AI-driven CVD diagnosis.The framework integrates cutting-edge techniques such as attention mechanisms, feature visualization, and rule-based interpretability to provide insights into how AI models derive their predictions.The study investigates how the XDL framework can improve stakeholder trust by enabling physicians to understand and validate the reasoning behind diagnostic outputs.Real-world datasets, including patient medical histories, imaging data, and electrocardiograms (ECGs), are utilized to develop and validate the proposed approach.By combining predictive accuracy with actionable explanations, the framework ensures that diagnostic models remain reliable and interpretable across diverse patient populations.Through extensive experiments, the paper demonstrates the framework's effectiveness in improving diagnostic confidence among healthcare providers while maintaining state-of-the-art performance metrics.Case studies highlight its practical application in detecting early-stage cardiovascular conditions such as arrhythmias and myocardial infarction.The research concludes by outlining strategies for integrating XDL into clinical workflows, fostering collaboration between AI systems and medical practitioners to enhance patient outcomes.This work underscores the importance of transparency in building trust and advancing the adoption of AI in critical healthcare applications.