Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term "eXplainable and Interpretable Artificial Intelligence" (XIAI) is introduced to distinguish XAI from IAI. Different models are further categorized based on their functionality (model-, input-, output-based) and scope (local, global). Our analysis shows that attention mechanisms are the most prevalent emerging IAI technique. The use of IAI is growing, distinguishing it from XAI. The major challenges identified are that most XIAI does not explore "global" modelling processes, the lack of best practices, and the lack of systematic evaluation and benchmarks. One important opportunity is to use attention mechanisms to enhance multi-modal XIAI for personalized medicine. Additionally, combining DL with causal logic holds promise. Our discussion encourages the integration of XIAI in Large Language Models (LLMs) and domain-specific smaller models. In conclusion, XIAI adoption in healthcare requires dedicated in-house expertise. Collaboration with domain experts, end-users, and policymakers can lead to ready-to-use XIAI methods across NLP and medical tasks. While challenges exist, XIAI techniques offer a valuable foundation for interpretable NLP algorithms in healthcare.

翻译：深度学习极大地提升了医疗健康研究中的自然语言处理能力。然而，基于深度学习的自然语言处理日益复杂的特性，使得模型需要具备透明的可理解性，或至少是可解释性，以实现可靠的决策。本文对医疗健康自然语言处理中可解释与可理解的深度学习进行了全面的范围综述。我们引入"可解释与可理解人工智能"这一术语，以区分可解释人工智能与可理解人工智能。进一步地，根据不同模型的功能性（基于模型、输入、输出）和范围（局部、全局）对其进行分类。分析表明，注意力机制是最常见的可理解人工智能新兴技术。可理解人工智能的应用正在增长，这使其与可解释人工智能区分开来。识别出的主要挑战包括：大多数可解释与可理解人工智能未探索"全局"建模过程，缺乏最佳实践，以及缺乏系统化评估和基准。一个重要机遇是利用注意力机制增强多模态可解释与可理解人工智能以支持个性化医疗。此外，将深度学习与因果逻辑相结合也颇具前景。我们的讨论鼓励将可解释与可理解人工智能集成到大语言模型和领域专用的小型模型中。结论是，在医疗健康领域采用可解释与可理解人工智能需要专门的内部专业知识。与领域专家、最终用户和政策制定者的协作，可催生适用于自然语言处理和医疗任务的可即用可解释与可理解人工智能方法。尽管存在挑战，可解释与可理解人工智能技术为医疗健康领域的可理解自然语言处理算法提供了宝贵的基础。