Retrieval-Augmented Large Language Models for Adolescent Idiopathic Scoliosis Patients in Shared Decision-Making

Wenqi Shi, Yuchen Zhuang, Yuanda Zhu, Henry J. Iwinski, J. Michael Wattenbarger, May Dongmei Wang

Abstract: As health-related decision-making evolves, patients increasingly seek help from additional online resources such as "Dr. Google" and ChatGPT. Despite their potential, these tools encounter limitations, including the risk of potentially inaccurate information, a lack of specialized medical knowledge, the risk of generating unrealistic outputs (hallucinations), and significant computational demands. In this study, we develop and validate an innovative shared decisionmaking (SDM) tool, Chat-Orthopedist, for adolescent idiopathic scoliosis (AIS) patients and families to prepare a meaningful discussion with clinicians based on retrieval-augmented large language models. Firstly, we establish an external knowledge base with information on AIS disease and treatment options Secondly, we develop a retrieval-augmented ChatGPT to feed LLMs with AIS domain knowledge, providing accurate and comprehensible responses to patient inquiries. In addition, we perform a cyclical process of human-in-the-loop evaluations for system validation and improvement. ment. Chat-Orthopedist may optimize SDM workflow by enabling better interactive learning experiences, more effective clinical visits, and better-informed treatment decision-making.