An Artificial Intelligence Tool for Understanding Brain-Gut Axis Symptom Patterns

The brain–gut axis constitutes a complex, bidirectional communication network through which psychological processes and gastrointestinal function continuously influence one another. Some individuals experience recurrent or long-standing patterns of overlapping psychological and digestive symptoms, making it difficult to determine whether these manifestations arise primarily from psychological factors, gastrointestinal dysfunction, or a combined interaction between both systems. Despite growing recognition of brain–gut interactions within contemporary medical research, early differentiation of such symptom patterns remains a practical challenge for both patients and healthcare systems Recent advances in artificial intelligence have expanded the potential of medical research and digital health tools, particularly in the analysis of complex, multidimensional symptom data. However, most existing AI-based medical applications primarily focus on supporting disease diagnosis, classification, or health risk prediction, offering limited assistance at the pre-diagnostic stage where individuals seek an initial understanding of their symptom patterns. This study introduces an artificial intelligence–assisted framework designed to provide preliminary, non-diagnostic guidance for individuals experiencing brain–gut axis–related symptom patterns. The framework is implemented as a standalone, publicly accessible digital platform that organizes self-reported symptom inputs across psychological, gastrointestinal, and relevant influencing domains, and analyzes their relative patterns to support early orientation. The resulting output is presented in an interpretable, user-centered format intended to be accessible to non-specialist users. By emphasizing early symptom-pattern understanding rather than diagnosis, this work presents a novel application of artificial intelligence within the context of brain–gut axis research and highlights the potential role of ethically designed digital health tools in supporting patient awareness and promoting more informed engagement with subsequent clinical care.