Palpation Alters Auditory Pain Expressions with Gender-Specific Variations in Robopatients

Sirithunge, Chapa, Xie, Yue, Nadipineni, Saitarun, Iida, Fumiya, Lalitharatne, Thilina Dulantha

arXiv.org Artificial Intelligence 

-- Diagnostic errors remain a major cause of preventable deaths, particularly in resource-limited regions. Medical training simulators, including robopatients, play a vital role in reducing these errors by mimicking real patients for procedural training such as palpation. However, generating multimodal feedback, especially auditory pain expressions, remains challenging due to the complex relationship between palpation behavior and sound. The high-dimensional nature of pain sounds makes exploration challenging with conventional methods. This study introduces a novel experimental paradigm for pain expressivity in robopatients where they dynamically generate auditory pain expressions in response to palpation force, by co-optimizing human feedback using machine learning. Using Proximal Policy Optimization (PPO), a reinforcement learning (RL) technique optimized for continuous adaptation, our robot iteratively refines pain sounds based on real-time human feedback. This robot initializes randomized pain responses to palpation forces, and the RL agent learns to adjust these sounds to align with human preferences. The results demonstrated that the system adapts to an individual's palpation forces and sound preferences and captures a broad spectrum of pain intensity, from mild discomfort to acute distress, through RL-guided exploration of the auditory pain space. The study further showed that pain sound perception exhibits saturation at lower forces with gender-specific thresholds. These findings highlight the system's potential to enhance abdominal palpation training by offering a controllable and immersive simulation platform. While specific statistics vary by region, diagnostic errors are a universal concern. Misdiagnoses may contribute to the nearly 7 million children who die each year from preventable causes, particularly in low-and middle-income countries [1]. These findings underscore the critical need for systemic improvements in diagnostic processes, enhanced communication among healthcare providers, and increased patient engagement to mitigate the risks associated with diagnostic errors. Palpation is one of the primary examination methods used by physicians to examine patients in various conditions ranging from simple abdominal pain to more serious conditions such as acute appendicitis and breast, soft tissue tumors.