Multimodal AI techniques for pain detection: integrating facial gesture and paralanguage analysis - Gutierrez, Rommel; Garcia-Ortiz, Joselin; Villegas-Ch, William (Frontiers in Computer Science, 2024)
Accurate pain detection is a critical challenge in healthcare, where communication and interpretation of pain often limit traditional subjective assessments. The current situation is characterized by the need for more objective and reliable methods to assess pain, especially in patients who cannot effectively communicate their experiences, such as young children or critically ill individuals. Despite technological advances, the effective integration of artificial intelligence tools for multifaceted and accurate pain detection continues to present significant challenges. Our proposal addresses this problem through an interdisciplinary approach, developing a hybrid model that combines the analysis of facial gestures and paralanguage using artificial intelligence techniques. This model contributes significantly to the field, allowing for more objective, accurate, and sensitive pain detection to individual variations. The results obtained have been notable, with our model achieving a precision of 92%, a recall of 90%, and a specificity of 95%, demonstrating evident efficiency over conventional methodologies. The clinical implications of this model include the possibility of significantly improving pain assessment in various medical settings, allowing for faster and more accurate interventions, thereby improving patients' quality of life.