Lingcheng Wang

Background: Artificial intelligence and large language models (LLMs) are emerging as transformative technologies in medicine. However, their ability to develop physical exercise rehabilitation programs and provide insights into musculoskeletal (MSK) disorders remains underexplored. This study aimed to evaluate the quality and readability of LLM-generated responses to consultation questions addressing various stages of the clinical process encountered by patients with MSK disorders. Methods: This study recruited 50 patients with musculoskeletal disorders and extracted disease-related frequently asked questions from Google search. We developed three clinical scenario-based question types simulating real consultations, which were processed by four LLMs (GPT-3.5-turbo, GPT-4-turbo, GPT-4o, and Claude-3-haiku-20240307). Response quality was assessed by orthopedic specialists and therapists using the DISCERN instrument, and GPT-4o-assisted evaluation was used to extend the assessment after validation with expert ratings. Readability was systematically assessed via six validated indices. Results: Among the 1476 LLM-generated responses, the generated rehabilitation programs demonstrated consistent adherence to the specified query requirements. The DISCERN scores ranged from 26 (poor) to 68 (excellent), with a mean score of 55.60 ± 8.40. The intraclass correlation coefficient (0.68) indicated moderate interrater agreement, and Cronbach’s α showed good internal consistency. The readability scores across six indices indicated that most responses exceeded the recommended reading levels (p < 0.05). Conclusions: LLMs generated moderate-to-high-quality PE rehabilitation recommendations for patients with MSK disorders across simulated consultation scenarios. However, limited supporting materials and suboptimal readability may restrict their effectiveness. With physician oversight, improved readability, and enhanced supplementary resources, LLMs demonstrate considerable potential as supportive tools in orthopedics.

Background: In recent studies, there have been no reports on the diagnostic value of musculoskeletal ultrasound in pediatric patients with a discoid lateral meniscus (DLM). This study aims to evaluate the diagnostic value of musculoskeletal ultrasound in pediatric DLM patients and to investigate potential correlations between ultrasound indices and subgroup classifications on the basis of ultrasound findings. Methods: This study included patients aged 18 years or younger from 1 September 2021, to 1 January 2023, who were diagnosed with lateral meniscus (LM) or DLM injuries through arthroscopic surgery. Ultrasound and MRI assessments were performed before surgery. Radiological measurements were conducted by one evaluator to determine the diagnostic value of the two methods. The McNemar–Bowker test was used to compare the sensitivity and specificity between the two methods. Spearman’s correlation was used for correlation analysis of different injury types. Cluster analysis was used to explore the basis for classifying the DLM. Results: This study included 72 patients (37 females and 35 males). The average age was 11.18 ± 4.25 years, and the BMI was 18.50 ± 3.46 kg/m ² . The results revealed the high diagnostic efficacy of ultrasound and MRI. There was no significant difference in sensitivity or specificity in diagnosing DLM via ultrasound compared with MRI ( P >0.05). Subgroup analysis did not reveal a significant difference between complete/incomplete DLM type or child/adolescent age ( P >0.05). Correlation analysis revealed that DLM patients were more likely to suffer from vertical tears (r = 0.34, P = 0.015) and locking symptoms (r = 0.36, P = 0.032). In the 30-degree flexion assessment, body thickness was useful for detecting vertical (r = 0.26, P = 0.049) and bucket tears (r = 0.3, P = 0.026). Cluster analysis of all DLM patients revealed two subtypes: DLM type I, with more anterior horn tears, and Type II DLM, with more popliteal hiatus tears ( P = 0.020). Conclusion: MUS has proven to be a valuable diagnostic tool with comparable diagnostic efficiency to that of MRI in pediatric DLM. Ultrasound indices correlated with different injury types and related symptoms. Cluster analysis revealed that pediatric DLMs can potentially be classified into two types. The two types of injuries differ in terms of injury area.