Advances in Machine Learning for Pain Recognition: A Review of Algorithms, Modalities, and Outcomes

The emergence of machine learning (ML) in healthcare has unlocked the transformative potential of pain detection and assessment. By providing new ways to address the challenges associated with subjective and inconsistent pain assessment methods. This review synthesizes recent advancements in ML-based approaches for pain detection, focusing on algorithms, modalities, and datasets. Techniques such as neural networks, transformer models, and multimodal fusion frameworks have demonstrated significant promise in identifying pain from diverse sources, including facial expressions, physiological signals, and synthetic datasets. Key contributions include novel transformer-based architectres like PainAttnNet for analyzing electrodermal activity [1], multimodal datasets such as BioVid for advancing pain classification [3], and innovative synthetic data generation pipelines to mitigate dataset scarcity and ethical concerns [1], [9]. Challenges related to dataset bias, interpretability, and real-world generalization remain pivotal, necessitating further research in integrating diverse data modalities and enhancing model robustness. This review underscores the role of ML as a cornerstone in advancing pain recognition, aiming to improve patient care and clinical decision-making through precise, automated, and scalable solutions.