From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?

Attention mechanisms are becoming the primary tool for making AI more interpretable in healthcare NLP, emerging as the most prevalent technique for helping clinicians understand how AI models make decisions, though the paper notes this focus on local explanations means we still lack understanding of how these systems work globally.

There's a critical gap between explainability and interpretability in healthcare AI: the paper distinguishes between XAI (explainable AI, which provides post-hoc explanations) and IAI (interpretable AI, built to be understandable from the start), with IAI adoption growing but still lacking standardized best practices and evaluation benchmarks needed for clinical deployment.

Current AI interpretability methods in healthcare are incomplete and lack systematic evaluation, with most approaches failing to explain the full decision-making process; the paper identifies the absence of standardized benchmarks and best practices as a major barrier to trustworthy AI implementation in medical settings.

Successful healthcare AI requires collaboration beyond computer scientists: the paper emphasizes that adopting interpretable AI in healthcare demands "dedicated in-house expertise" and meaningful collaboration with clinicians, patients, policymakers, and domain experts to create practical, usable systems rather than purely technical solutions.

Combining deep learning with causal reasoning and multi-modal approaches offers promise for personalized medicine, suggesting that future healthcare AI should move beyond pattern recognition alone to incorporate causal logic and integrate multiple data types (text, images, etc.) with interpretability built in.