mlsanityData to 7 Oct 2026

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

arXiv 2309.00252 ↗First posted 1 Sep 2023cs.CV cs.LGcited by 4

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Abstract (arXiv)

Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand-supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine-learning approaches, deep-learning models are complex and are often treated as a "black box" that can cause uncertainty regarding how they operate. Explainable Artificial Intelligence (XAI) refers to methods that explain and interpret machine learning models' inner workings and how they come to decisions, which is especially important in the medical domain to guide the healthcare decision-making process. This review summarises recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 38 of its 76 references that are in the map. First posted on arXiv 1 Sep 2023.

Builds on

38 of 76 references.

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26 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025X-Ray-CoT: Interpretable Chest X-ray Diagnosis with Vision-Language Models via Chain-of-Thought Reasoningcited by 4
  2. 2024Developing an Explainable Artificial Intelligent (XAI) Model for Predicting Pile Driving Vibrations in Bangkok's Subsoilcited by 0
  3. 2025Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformerscited by 0
  4. 2025Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profilescited by 0