XAI methods for precision medicine in medical decision support systems
Abasiama Godwin Akpan, Flavious Bobuin Nkubli, Jeremiah Chinonso Mbazor, Geofery Luntsi, Offiong Udeme · 2022
Over the last couple of years, explainable artificial intelligence (XAI) has witnessed tremendous development evidenced by growing research interest in the area. This could be attributed to the increasing role of machine learning, especially deep learning. While these models are highly accurate, they lack explainability and interpretability. There has been limited application of AI systems in vital fields such as precision medicine due to the aforementioned vagueness. The aim of the study is XAI for precision medicine in medical decision support systems (MDSS). The authors outline through an organized examination of literature the application of XAI in MDSS, thus highlighting the several benefits of the use of XAI as reported in the literature such as enhanced decision confidence in precision medicine. The opportunities and challenges of explainable models in MDSS were discussed. Guidelines for the implementation of XAI in MDSS have been recommended in this study while highlighting some of the opportunities and challenges.