Ethical Challenges of Artificial Intelligence-Based Pain Prediction in Animal Models Using Machine Learning and Deep Learning

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising approach for improving pain assessment and prediction in laboratory animal models. Conventional pain evaluation methods often rely on behavioral observation and expert-based scoring, which may be affected by subjectivity, limited sensitivity, and inter-observer variability. AI-driven approaches provide opportunities to analyze complex behavioral, facial, physiological, and multimodal biological patterns, enabling more objective, automated, and continuous assessment of animal pain states. However, despite their technological advantages, the implementation of AI in animal pain prediction introduces important ethical, methodological, and translational challenges. Issues related to dataset quality, algorithmic bias, model interpretability, biological variability, generalizability, and human oversight must be carefully addressed to ensure responsible application. This review examines the current landscape of AI-based pain prediction in animal models, emphasizing the intersection between computational innovation and ethical responsibility. The potential contribution of AI to animal welfare is discussed through its ability to enhance pain recognition, support refinement strategies, and reduce unnecessary suffering. Furthermore, technical considerations including standardized data collection, robust validation, explainable AI, and multidisciplinary collaboration are highlighted as essential components for trustworthy AI development. Future perspectives suggest that ethical AI frameworks integrating advanced computational models with veterinary expertise and welfare principles may transform biomedical research by providing more accurate pain assessment while promoting responsible and humane experimental practices.