TY  - JOUR
TI  - Artificial intelligence strategies for emotion recognition in cancer pain research
AU  - Cascella, Marco
AU  - Zarrella, Adele
AU  - Conti, Valeria
AU  - Filippelli, Amelia
AU  - Bruno, Maria Pia
AU  - Esposito, Dalila
AU  - Feo, Rosario De
AU  - Cerrone, Valentina
AU  - Guerra, Cosimo
AU  - Lisio, Flavio Di
AU  - Sabbatino, Francesco
AU  - Piazza, Ornella
AU  - Cirillo, Stefano
AU  - Polese, Giuseppe
PY  - 2026
JO  - Exploration of Medicine
VL  - 7
SP  - 1001404
DO  - 10.37349/emed.2026.1001404
UR  - https://www.explorationpub.com/Journals/em/Article/1001404
AB  - Although emotions play a fundamental role in modulating pain perception, their objective assessment in clinical contexts remains challenging. Recent advances in artificial intelligence (AI) have opened new opportunities to measure emotional states through facial expression analysis, physiological signal modeling, natural language processing (NLP), and multimodal data integration. In affective computing, the field that focuses on technologies designed to recognize, interpret, process, and simulate human emotions, facial expression-based emotion recognition has progressed from traditional machine learning methods to advanced deep learning approaches, including convolutional neural networks (CNNs), attention-based hybrid models, and transformer architectures. Similarly, recurrent neural networks and self-supervised learning methods have been implemented for developing models from physiological signals such as electrocardiography, photoplethysmography, galvanic skin response, and related biosignals. Additionally, NLP systems can extract affective information from naturalistic text, using both lexicon-based and transformer-based models. Finally, multimodal fusion and alignment techniques allow the integration of heterogeneous data streams, providing richer and more ecologically valid emotion representations. Collectively, these strategies offer powerful tools for advancing automatic pain assessment (APA) in cancer care, with the potential to support personalized, emotion-aware therapeutic approaches. However, from an AI perspective, several open challenges remain, including multimodal representation learning under weak supervision, robustness to missing or degraded modalities, limited explainability of affective inference models, lack of standardized benchmarking protocols, and the presence of bias and domain shift in emotion datasets. Given the inherently subjective, context-dependent, and culturally mediated features of the emotional experience, further research is needed to address these technical limitations, integrating technological advances with the intrinsic complexity of emotion interpretation.
ER  -