<![CDATA[In the medical field, accurate pain assessment poses a significant challenge. This study explores the use of facial expression recognition technology to detect pain, with a particular focus on the sensitive area around the eyes. A comprehensive review of 50 articles in the field suggests that changes in facial expressions, including forehead wrinkles, drooping eyebrows, and eyelid changes, can serve as significant indicators of pain. The integration of advanced sensors with deep learning and machine learning algorithms has demonstrated significant potential in the effective recognition of pain indicators. Recent innovations, including more advanced neural networks and biological marker measurements, offer efficient solutions for real-time applications. Nevertheless, challenges persist, including variations in individual expression and limitations in high-quality datasets. Consequently, this study proposes the development of more diverse pain expression datasets and the optimization of algorithms for this technology. Integration of systems with facial expression-based pain scales and the conduction of clinical trials are pivotal in enhancing the accuracy and reliability of pain assessment, thereby facilitating healthcare professionals in caring for patients and improving the quality of healthcare services.]]>
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