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

AI is increasingly used in healthcare to help address doctor shortages, with Vision Transformers (ViT) representing state-of-the-art technology for analyzing medical images, but these advanced systems often operate as "black boxes" where it's unclear how they reach diagnostic conclusions.

Explainable AI (XAI) is critical for medical applications because healthcare decisions directly impact patient safety and outcomes; the paper emphasizes that understanding how an AI system arrives at a diagnosis—not just what diagnosis it suggests—is essential for clinicians to trust and appropriately use these tools.

Current deep-learning models lack transparency compared to traditional approaches, creating uncertainty about their decision-making processes; this opacity is particularly problematic in medicine where clinicians need to understand the reasoning behind AI recommendations to make informed clinical judgments.

Self-attention mechanisms in Vision Transformers can be interpreted to show which parts of medical images the AI focuses on when making decisions, offering a pathway toward more transparent AI systems that can highlight relevant diagnostic features to healthcare providers.

The review advocates for combining advanced AI capabilities with interpretability methods to enable responsible AI adoption in healthcare, ensuring that powerful diagnostic tools remain understandable and accountable to medical professionals and patients.