<![CDATA[Objective pain detection remains a major challenge in healthcare because conventional assessments rely on subjective self-reports. Advances in artificial intelligence (AI) have enabled more objective approaches by analyzing biological signals and human expressions. Facial expressions and functional Near-Infrared Spectroscopy (fNIRS) are widely studied due to their complementary characteristics. Facial expressions are non-invasive, easy to capture, and strongly associated with visible pain responses, making them suitable for real-time applications. In contrast, fNIRS measures brain activity related to pain perception, providing a more objective physiological perspective. This study follows PRISMA guidelines for systematic literature review. Studies were identified from major databases, screened for relevance, assessed for eligibility, and included for final analysis. A total of 45 studies were selected. Previous research shows that AI, especially deep learning, is effective in analyzing facial expressions and fNIRS signals for pain detection. However, few studies systematically compare these modalities in a unified framework. This review highlights a shift from single-modality to hybrid and multimodal approaches integrating facial and fNIRS data. Deep learning models, particularly CNNs for facial analysis and hybrid machine learning–deep learning methods for physiological signals, dominate recent studies. Multimodal fusion consistently outperforms single-modality approaches, improving accuracy and robustness in pain detection tasks]]>
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