Unleashing the Power of XAI (Explainable Artificial Intelligence)
G. Arun Sampaul Thomas, Subburaj Muthukaruppasamy, Jothiperumal Nandha Gopal, G. Sudha, K. Aanandha Saravanan · 2024
The performance of artificial intelligence (AI) system is approaching and even surpassing that of humans for an expanding variety of complicated activities due to the contemporary progressions in machine learning, deep learning, and the further approachability of massive datasets. Conversely, these practices are utilised as “black boxes”. It means that no prior knowledge is accessible concerning the decision-making process with its composite and non-linear structure. The growth of methodologies for interpretability, understanding, and data visualisation has recently drawn a lot of interest because its lack of transparency can be a substantial disadvantage in some fields, such as medicine and healthcare. The term “explainable Artificial Intelligence (XAI)” is used to describe this new extent of analysis. This chapter aims to frame the theme and current state of XAI in the healthcare industry. Moreover, it highlights some of the difficulties that confront and the issues that still need to be resolved. Better decision-making is possible using XAI. It can assist, but the research warns that it will be a trap since current methods are not enough. XAI has numerous challenges, including integrating into machine learning or deep learning algorithmic models, its workflow and different use cases, organisational concerns, medical expert-friendly explanations, and privacy issues. Because health is constantly at risk, algorithms and models must be reliable. An emerging field is XAI. Although there are still many challenges and research gaps, the development of XAI in medicine and digital healthcare will help to generalise AI in clinical practice by merging non-imaging clinical data with cross-modality imaging. The growth in the XAI healthcare software market is rapid. The global market for explainable AI (XAI) is expected to grow from $3.5 billion in 2020 to $21 billion by 2030, according to a description by Research and Markets. The benefits of AI will become increasingly obvious as the volume of data creation increases. The defined framework to be agreed upon soon strikes a balance between data security and potential benefits for government, business, and advocacy organisations. According to a report by the World Economic Forum, as a confluence of new use cases will produce “a genuinely proactive, predictive healthcare system,” and the year 2030 will be a watershed moment for AI in healthcare.