Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Ravichandra Madanu, Maysam F. Abbod*, Fu Jung Hsiao, Wei Ta Chen, Jiann Shing Shieh*

*此作品的通信作者

研究成果: Review article同行評審

7 引文 斯高帕斯(Scopus)

摘要

Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.

原文English
文章編號74
期刊Technologies
10
發行號3
DOIs
出版狀態Published - 6月 2022

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