Summary of From Explainable to Interpretable Deep Learning For Natural Language Processing in Healthcare: How Far From Reality?, by Guangming Huang et al.
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
by Guangming Huang, Yingya Li, Shoaib Jameel, Yunfei Long, Giorgos Papanastasiou
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a comprehensive scoping review of explainable and interpretable deep learning (DL) models in the context of natural language processing (NLP) in healthcare. The authors introduce the term “eXplainable and Interpretable Artificial Intelligence” (XIAI) to distinguish it from interpretability AI (IAI). They categorize various DL models based on their functionality and scope, highlighting attention mechanisms as a prominent emerging technique. The study identifies challenges in XIAI adoption, including the lack of best practices, systematic evaluation, and benchmarks. However, opportunities for integrating XIAI with multi-modal approaches and causal logic are explored. The authors emphasize the importance of combining DL with domain-specific expertise to develop interpretable NLP algorithms in healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Explainable Artificial Intelligence (XAI) is a crucial aspect of deep learning (DL) in healthcare research, as it enhances decision-making reliability. This paper reviews the current state of XAI and interpretable DL models in healthcare natural language processing (NLP). The authors find that attention mechanisms are key to emerging IAI techniques. While challenges persist, including a lack of best practices, evaluation, and benchmarks, opportunities arise from combining XAI with multi-modal approaches and causal logic. The study concludes by emphasizing the need for domain-specific expertise and collaboration. |
Keywords
* Artificial intelligence * Attention * Deep learning * Multi modal * Natural language processing * Nlp
