Purpose: Large language models (LLM) have the potential to bridge knowledge gaps in patient education and enrich patient-surgeon interactions. This study evaluated three chatbots for delivering empathetic and precise adolescent idiopathic scoliosis (AIS) related information and management advice. Specifically, we assessed the accuracy, clarity, and relevance of the information provided, aiming to determine the effectiveness of LLMs in addressing common patient queries and enhancing their understanding of AIS. Methods: We sourced 20 webpages for the top frequently asked questions (FAQs) about AIS and formulated 10 critical questions based on them. Three advanced LLMs—ChatGPT 3.5, ChatGPT 4.0, and Google Bard—were selected to answer these questions, with responses limited to 200 words. The LLMs’ responses were evaluated by a blinded group of experienced deformity surgeons (members of the European Spine Study Group) from seven European spine centers. A pre-established 4-level rating system from excellent to unsatisfactory was used with a further rating for clarity, comprehensiveness, and empathy on the 5-point Likert scale. If not rated 'excellent', the raters were asked to report the reasons for their decision for each question. Lastly, raters were asked for their opinion towards AI in healthcare in general in six questions. Results: The responses among all LLMs were ‘excellent’ in 26% of responses, with ChatGPT-4.0 leading (39%), followed by Bard (17%). ChatGPT-4.0 was rated superior to Bard and ChatGPT 3.5 (p = 0.003). Discrepancies among raters were significant (p < 0.0001), questioning inter-rater reliability. No substantial differences were noted in answer distribution by question (p = 0.43). The answers on diagnosis (Q2) and causes (Q4) of AIS were top-rated. The most dissatisfaction was seen in the answers regarding definitions (Q1) and long-term results (Q7). Exhaustiveness, clarity, empathy, and length of the answers were positively rated (> 3.0 on 5.0) and did not demonstrate any differences among LLMs. However, GPT-3.5 struggled with language suitability and empathy, while Bard’s responses were overly detailed and less empathetic. Overall, raters found that 9% of answers were off-topic and 22% contained clear mistakes. Conclusion: Our study offers crucial insights into the strengths and weaknesses of current LLMs in AIS patient and parent education, highlighting the promise of advancements like ChatGPT-4.o and Gemini alongside the need for continuous improvement in empathy, contextual understanding, and language appropriateness.

» Congenital scoliosis is caused by developmental abnormalities such as failure of segmentation, failure of formation, or a combination of these conditions. » The multi-organ nature of congenital scoliosis necessitates a team approach in which spinal and intraspinal deformities are treated by orthopaedic surgeons, neurosurgeons, and physical therapists, and associated anomalies are treated by pediatricians, pediatric surgeons, cardiologists, pulmonologists, and urologists. » At the initial time of diagnosis, observation is appropriate for most cases. Although early arthrodesis may be appropriate for select cases, delaying tactics that are nonoperative (bracing and casting) or operative (growing rod and growth guidance) may preserve (or even stimulate) spine and chest cage development while controlling the deformity. » Early hemivertebra resection and short-segment arthrodesis provide excellent outcomes. » Patients with a neurological deficit may require neurosurgical intervention, which may be performed either before a deformity correction surgical procedure or simultaneously with the procedure.

Background: Advancements in non-ionizing methods for quantifying spinal deformities are crucial for assessing and monitoring scoliosis. In this study, we analyzed the observer variability of a newly developed digital tool for quantifying body asymmetry from clinical photographs. Methods: Prospective observational multicenter study. Initially, a digital tool was developed using image analysis software, calculating quantitative measures of body asymmetry. This tool was integrated into an online platform that exports data to a database. The tool calculated 10 parameters, including angles (shoulder height, axilla height, waist height, right and left waistline angles, and their difference) and surfaces of the left and right hemitrunks (shoulders, waists, pelvises, and total). Subsequently, an online training course on the tool was conducted for twelve observers not involved in its development (six research coordinators and six spine surgeons). Finally, 15 standardized back photographs of adolescent idiopathic scoliosis patients were selected from a multicenter image bank, representing various clinical scenarios (different age, gender, curve type, BMI, and pre- and postoperative images). The 12 observers measured the photographs at two different times with a three-week interval. For the second round, the images were randomly mixed. Inter- and intra-observer variabilities of the measurements were analyzed using intraclass correlation coefficients (ICCs), and reliability was measured by the standard error of measurement (SEM). Group comparisons were made using Student’s t-test. Results: The mean inter-observer ICC for the ten measurements was 0.981, the mean intra-observer ICC was 0.937, and SEM was 0.3–1.3°. The parameter with the strongest inter- and intra-observer validity was the difference in waistline angles 0.994 and 0.974, respectively, while the highest variability was found with the waist height angle 0.963 and 0.845, respectively. No test–retest differences (p > 0.05) were observed between researchers (0.948 ± 0.04) and surgeons (0.925 ± 0.05). Conclusion: We developed a new digital tool integrated into an online platform demonstrating excellent reliability and inter- and intra-observer variabilities for quantifying body asymmetry in scoliosis patients from a simple clinical photograph. The method could be used for assessing and monitoring scoliosis and body asymmetry without radiation.

There is a paucity of knowledge about benign bone lesions. The advances in imaging methods can screen bone lesions incidentally, and missing information can be provided. The aim of the study is to collect information about the prevalence and natural history of benign bone lesions with the use of whole-body biplanar slot-scanning imaging (EOS). EOS images acquired between 2015 and 2020 were retrospectively analyzed. Anatomical locations of lesions, number of lesions with polyostotic involvement and radiographic features of each were recorded. Fibrous lesions were further categorized according to the transition stages. The natural course was noted as remained in the same stage, progressed and disappeared during follow-up. A total of 1944 EOS images of 1378 (936 women and 442 men) patients were analyzed. The mean age was 12.3 (5–18) years. Bone lesions of the lower extremities were found in 278 scans (14.3%) of 196 (139 women and 57 men) patients (14.2%). Monostotic lesions were observed in 172 patients, and 24 had polyostotic lesions. The prevalence of lesions was 10.5%, 1.8%, 1.7%, 1.7% and 1.4‰ for fibrous cortical defect (FCD), nonossifying fibroma (NOF), osteochondroma, bone island and simple bone cyst, respectively. Among 145 FCDs, 55.2% of the lesions were stage A, 27.6% were stage B, 9.6% were stage C and 7.5% were stage D. EOS images acquired predominantly for spinal pathologies revealed a prevalence of 14% of benign bone tumors in the lower extremities. With the developments in imaging methods, the probability of encountering incidental lesion increases, and information about bone pathologies can be gathered.