Language-specific representation of emotion-concept knowledge causally supports emotion inference

Integrating generative AI with neurophysiological methods in psychiatric practice

This paper explores the potential integration of generative AI (e.g., large language models) with neuroscientific and physiological approaches in psychiatric practice. Renowned for its advanced natural language processing capabilities, generative AI has shown promise in psychological counseling, emotional support, and clinical interventions. However, its application alongside neuroscience and physiology in psychiatry remains underexplored. We propose that generative AI can facilitate translations and adaptive explanations, streamline experimental preparation, enhance multi-modal data analysis, and improve clinical applications through real-time communication, content generation, and data synthesis. Furthermore, we examine how generative AI, as a specialized application of deep learning, can identify new biomarkers and construct neurophysiological models of psychiatric symptoms. We also discuss the synergistic relationship between neuroscience and AI development, particularly in improving AI's emotional recognition and learning mechanisms. While acknowledging the potential benefits, we address the challenges and risks associated with generative AI in psychiatry, including data reliability, privacy concerns, and resource constraints. This perspective advocates for a balanced approach to leveraging AI's capabilities while safeguarding mental health.