A review of Explainable Artificial Intelligence in healthcare

Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Cifci, Samina Kausar, Rizwan Rehman, Priyakshi Mahanta, Pranjal Kumar Bora, Ammar Almasri, Rami S. Alkhawaldeh, Sadiq Hussain, Bilal Alatas, Afshin Shoeibi, Hossein Moosaei, Milan Hladík, Saeid Nahavandi, Panos M. Pardalos

Abstract: Highlights•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.