A review of Explainable Artificial Intelligence in healthcare

• Emphasizes the need for transparency to build healthcare professionals' trust in AI systems. • Addresses the critical need for explainability due to potential high-impact consequences of AI errors in healthcare. • Categorizes XAI methods into six groups for healthcare research: feature-oriented, global, concept, surrogate, local pixel-based, and human-centric. • Analyzes the significance of XAI in overcoming healthcare-specific challenges. • Provides an exhaustive review of XAI applications and relevant experimental results in healthcare contexts. Explainable Artificial Intelligence (XAI) encompasses the strategies and methodologies used in constructing AI systems that enable end-users to comprehend and…

• GA
  Grantová Agentura České Republiky

  Awards: 22-11117S, 22-19353S
• NR
  National Research University Higher School of Economics