Advancing Genomic Interpretation: XAI in Deep Learning Models for Precision Medicine

This narrative review explores the transformative role of Explainable Artificial Intelligence (XAI) in enhancing the interpretability of deep learning models for genomic data analysis within precision medicine. The primary objective is to synthesize recent developments, challenges, and opportunities in applying XAI techniques, such as SHAP, LIME, and Grad-CAM, to models like CNNs, RNNs, and Transformers, thereby fostering transparency and trust in clinical applications. The review traces the historical evolution from statistical genomic analysis to AI-driven approaches, highlighting the emergence of XAI as a response to the 'black-box' nature of deep learning. Key themes include the technical foundations of XAI, its clinical applications in disease prediction and drug response modeling, and its societal implications for equitable healthcare. Significant findings reveal the widespread adoption of XAI to elucidate genetic variant contributions, though challenges like computational costs, data biases, and lack of standardized evaluation metrics persist. Critical reflections underscore gaps in multi-omics integration, stakeholder-specific explanations, and fairness in genomic applications. The review concludes that XAI significantly advances genomic interpretation by bridging computational predictions with clinical utility, yet addressing ethical and scalability issues is crucial for its full integration into precision medicine. By offering a cohesive narrative, this review contributes to understanding how XAI can reshape personalized healthcare through transparent and actionable genomic insights.

کلیدواژه ها:

Deep Learning ، Explainable AI ، Genomic Data Analysis ، Precision Medicine ، SHAP ، Transparency