Evidence record 5014 · automatically gathered

SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT

Optical coherence tomography (OCT), a commonly used retinal imaging modality, plays a central role in retinal disease diagnosis by providing high-resolution visualization of retinal layers. While deep learning (DL) has achieved expert-level accuracy in OCT-based retinal disease detection, its "black box" nature poses challenges for clinical adoption, where explainability is essential for clinical trust and regulatory approval. Existing post-hoc explainable AI (XAI) methods often struggle to deli

Record details

Published: 4 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Healthcare · Transparency
Retrieved: 14 July 2026

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ethics.ai (4 May 2026), “SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT,” evidence record 5014, https://ethics.ai/record/5014 (originally published by arXiv).

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