Deep learning (DL) has substantially enhanced healthcare research by
addressing various natural language processing (NLP) tasks. Yet, the increasing
complexity of DL-based NLP methods necessitates transparent model
interpretability, or at least explainability, for reliable decision-making.
This work presents a thorough scoping review on explainable and interpretable
DL in healthcare NLP. The term "XIAI" (eXplainable and Interpretable Artificial
Intelligence) was introduced to distinguish XAI from IAI. Methods were further
categorized based on their functionality (model-, input-, output-based) and
scope (local, global). Our analysis shows that attention mechanisms were the
most dominant emerging IAI. Moreover, IAI is increasingly used against XAI. The
major challenges identified are that most XIAI do not explore "global" modeling
processes, the lack of best practices, and the unmet need for systematic
evaluation and benchmarks. Important opportunities were raised such as using
"attention" to enhance multi-modal XIAI for personalized medicine and combine
DL with causal reasoning. Our discussion encourages the integration of XIAI in
LLMs and domain-specific smaller models. Our review can stimulate further
research and benchmarks toward improving inherent IAI and engaging complex NLP
in healthcare.

深度学习在医疗保健研究中通过解决各种自然语言处理（NLP）任务得到了极大的增强。本文对可解释和可解释的医疗 NLP 中的深度学习进行了彻底的综述。发现注意力机制是最主要的新兴 IAI，同时也提出了重要的机会，如利用注意力提升个性化医学的多模态 XIAI 并将 DL 与因果推理相结合。