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*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Article number74
JournalTechnologies
Volume10
Issue number3
DOIs
StatePublished - Jun 2022

Keywords

  • artificial intelligence
  • explainable AI
  • healthcare
  • neural networks
  • pain

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