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

🤖 Plain-English Summary

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.

🔑 Key Findings

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

🔗 Related Resources