A review of Explainable Artificial Intelligence in healthcare

作者：Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Çifçi, Samina Kausar, Rizwan Rehman, Priyakshi Mahanta, Pranjal Kumar Bora, Ammar Almasri, Rami S. Alkhawaldeh, Sadiq Hussain, Bilal Alataş, Afshin Shoeibi, Hossein Moosaei, Milan Hladík, Saeid Nahavandi, Pãnos M. Pardalos · 发表于：Computers & Electrical Engineering · 年份：2024 · DOI：10.1016/j.compeleceng.2024.109370 · 被引用次数：561 · 研究领域：Explainable Artificial Intelligence (XAI)、Machine Learning in Healthcare、Artificial Intelligence in Healthcare and Education

Explainable Artificial Intelligence (XAI) encompasses the strategies and methodologies used in constructing AI systems that enable end-users to comprehend and interpret the outputs and predictions made by AI models. The increasing deployment of opaque AI applications in high-stakes fields, particularly healthcare, has amplified the need for clarity and explainability. This stems from the potential high-impact consequences of erroneous AI predictions in such critical sectors. The effective integration of AI models in healthcare hinges on the capacity of these models to be both explainable and interpretable. Gaining the trust of healthcare professionals necessitates AI applications to be transparent about their decision-making processes and underlying logic. Our paper conducts a systematic review of the various facets and challenges of XAI within the healthcare realm. It aims to dissect a range of XAI methodologies and their applications in healthcare, categorizing them into six distinct groups: feature-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric approaches. Specifically, this study focuses on the significance of XAI in addressing healthcare-related challenges, underscoring its vital role in safety-critical scenarios. Our objective is to provide an exhaustive exploration of XAI's applications in healthcare, alongside an analysis of relevant experimental outcomes, thereby fostering a holistic understanding of X...