Objective To compare the performance of ChatGPT-4.5 and DeepSeek-V3 across five key domains of physical therapy for knee osteoarthritis (KOA), evaluating the accuracy, completeness, reliability, and readability of their responses and exploring their clinical application potential. Methods Twenty-one core questions were extracted from 10 authoritative KOA rehabilitation guidelines published between September 2011 and January 2024, covering five task categories: rehabilitation assessment, physical agent modalities, exercise therapy, assistive device use, and patient education. Responses were generated using both the ChatGPT-4.5 and DeepSeek-V3 models and evaluated by four physical therapists with over five years of clinical experience using Likert scales (accuracy and completeness: 5 points; reliability: 7 points). The scale scores were compared between the two large language models. Additional assessment included language style clustering. Results Most of the scale scores did not follow a normal distribution, and were presented as median (lower quartile, upper quartile). ChatGPT-4.5 outperformed DeepSeek-V3 with higher scores in accuracy [4.75 (4.75, 4.75) vs. 4.75 (4.50, 5.00), P=0.018], completeness [4.75 (4.50, 5.00) vs. 4.25 (4.00, 4.50), P=0.006], and reliability [5.75 (5.50, 6.00) vs. 5.50 (5.50, 5.50), P=0.015]. Clustering analysis of language styles revealed that ChatGPT-4.5 demonstrated a more diverse linguistic style, whereas DeepSeek-V3 responses were more standardized. ChatGPT-4.5 achieved higher scores than DeepSeek-V3 in lexical richness [4.792 (4.720, 4.912) vs. 4.564 (4.409, 4.653), P<0.001], but lower than DeepSeek-V3 in syntactic richness [2.133 (2.072, 2.154) vs. 2.187 (2.154, 2.206), P=0.003]. Conclusions ChatGPT-4.5 demonstrates superior performance in accuracy, completeness, and reliability, indicating a stronger capacity for task execution. It uses more diverse words and has stronger flexibility in language generation. DeepSeek-V3 exhibited greater syntactic richness and is more normative in language. ChatGPT-4.5 is better suited for content-rich tasks that require detailed explanation, while DeepSeek-V3 is more appropriate for standardized question-answering applications.
Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for representing PTSD is limited by the expressiveness of existing models, which does not yield optimal classification results. To address this, we proposed a multi-graph multi-kernel graph convolutional network (MK-GCN) model for classifying PTSD data. First, we constructed functional connectivity matrices at different scales for the same subjects using different atlases, followed by employing the k-nearest neighbors algorithm to build the graphs. Second, we introduced the MK-GCN methodology to enhance the feature extraction capability of brain structures at different scales for the same subjects. Finally, we classified the extracted features from multiple scales and utilized graph class activation mapping to identify the top 10 brain regions contributing to classification. Experimental results on seismic-induced PTSD data demonstrated that our model achieved an accuracy of 84.75%, a specificity of 84.02%, and an AUC of 85% in the classification task distinguishing between PTSD patients and non-affected subjects. The findings provide robust evidence for the auxiliary diagnosis of PTSD following earthquakes and hold promise for reliably identifying specific brain regions in other PTSD diagnostic contexts, offering valuable references for clinicians.