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

By Guang Yang, Qinghao Ye, Jun Xia. Information Fusion, 2021

630 citations recorded by the source corpus.

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems’ black-box choices are made. 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. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. 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. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/750dc68ffb567d79f4d61a99aa347cc0343e65c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

R. Gururajan
Xujuan Zhou
T. Shaik
U. R. Acharya
Lin Li
Niall Higgins
Chiwei Ong
Milan Toma

Citations in the corpus

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives (2024, background): “Instead, it leverages the power of data analysis to identify patterns and trends in speciﬁc characteristics for accomplishing complex tasks [21].”
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, neutral): “In the following sections, we analyze what LLMs can do for Healthcare in detail.”
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “These models cannot elaborate their results and provide cause-and-effect relationships between predictor variables and target variables (G. Yang et al., 2022).”