From explainable to interpretable deep learning for natural language processing in healthcare: how far from reality?

arXiv:2403.11894v1 Announce Type: cross
Abstract: 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-, …

abstract arxiv complexity cs.ai cs.cl cs.lg decision deep learning explainability healthcare interpretability language language processing least making model interpretability natural natural language natural language processing nlp processing reality research tasks type work