Evaluating artificial intelligence-generated clinical guidance for patellar dislocation: accuracy, readability and qualitative appraisal of DeepSeek-R1 responses.

Introduction: Large language models (LLMs) are increasingly used to answer patient questions, but the readability and information quality of patellar dislocation guidance remain unclear. Methods: A series of clinical questions covering etiopathogenesis, mechanisms, clinical manifestations, diagnosis, treatment, complications, and postoperative rehabilitation of patellar dislocation were posed to DeepSeek-R1 v2.3.1 and ChatGPT-4o. Readability was assessed using the Flesch Reading Ease score (FRES), Flesch-Kincaid Grade Level (FKGL), Automated Readability Index (ARI), SMOG Index, and Gunning Fog Score (GFS). Three sports medicine surgeons rated responses using DISCERN. Wilcoxon signed-rank tests compared models; Inter-rater reliability (IRR) for DISCERN was assessed with intraclass correlation coefficient (ICC). Additionally, we analysed DeepSeek's responses based on the current literature and therapeutic standards. Results: Readability favored ChatGPT-4o across all indices (between-model Conclusions: Although both AI platforms can generate clinically relevant responses of moderate quality, they should be regarded as adjunctive information rather than stand-alone clinical guidance, particularly given the high incidence of patellar instability and developmental characteristics of children and adolescents. Exploratory qualitative assessment of DeepSeek-R1 responses identified strengths in clinical explanation and reasoning, while also revealing limitations in citation reliability and readability. Future work should incorporate multi-turn, multi-generation frameworks, more samples with repeated sampling and longitudinal assessment, enforced citation standards, and expanded evaluation to better quantify both clinical utility and reliability.