Explainable AI: A Systematic Literature Review Focusing on Healthcare

The integration of Artificial Intelligence (AI) in healthcare holds immense promise for revolutionizing clinical practices and patient outcomes. However, the lack of transparency in AI decision-making processes poses significant challenges, hindering trust and understanding among healthcare professionals. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to address these concerns by shedding light on AI model predictions and enhancing interpretability. This review explores the efficacy and applications of XAI within the healthcare domain, focusing on key research questions regarding challenges, effectiveness, and utilized algorithms. Through a comprehensive examination of 50 recent literature, we identify challenges related to the integration of XAI into clinical workflows, the necessity for validation and trust-building, and technical hurdles such as diverse explanation methods and data quality issues. Popular XAI algorithms such as SHAP, LIME, and GRAD-CAM demonstrate significant promise in clarifying model predictions and aiding in the interpretation of AI-driven healthcare systems. Overall, this review underscores the immense potential of XAI in revolutionizing healthcare delivery and decision-making processes, emphasizing the need for further research and development to address challenges and leverage its full potential in enhancing healthcare practices.