Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems’ black-box choices are made. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly.
• 2 Many of the machine learning algorithms cannot manifest how and why a decision has been cast.
• 3 This is particularly true of the most popular deep neural network approaches currently in use.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: