ArticleSpine deformity2026
Evaluation of Google and ChatGPT responses to common patient questions about scoliosis.
Article in Spine deformity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Evaluation of ChatGPT-5 responses to patient-centered questions on stromal vascular fraction for knee osteoarthritis: fair to good quality and content.BMC musculoskeletal disorders · 2026Article
- Evaluating large language models as tools for public health education on scoliosis.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveScoliosis is primarily seen during adolescence and often causes significant concern among patients and their families when the deformity becomes noticeable. With technological advancements, patients frequently search the Internet for information regarding their disease's diagnosis, treatment, prognosis, and potential complications. This study aims to assess the quality of Google and ChatGPT responses to questions about scoliosis.
methodsA search was conducted using Google with the keyword "scoliosis." The first ten questions listed under the "People Also Ask" (FAQs) section were recorded. Responses to these questions from ChatGPT and Google were evaluated using a four-level rating system: "Excellent response not requiring clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," and "unsatisfactory requiring substantial clarification." Additionally, the sources of the responses were categorized as academic, commercial, medical practice, governmental, or social media.
resultsChatGPT provided "excellent responses requiring no explanation" for 9 out of 10 questions (90%). In contrast, none of Google's responses were categorized as excellent; 50% were unsatisfactory, requiring substantial clarification; 40% were satisfactory, requiring moderate clarification, and 10% were satisfactory, requiring minimal clarification. ChatGPT sourced 60% of its responses from academic resources and 40% from medical practice websites. Conversely, Google did not use scholarly sources, with 50% of reactions derived from commercial websites, 30% from medical practice sources, and 20% from social media. When the agreement between the 4 raters, regardless of AI, was examined using Fleiss Multirater Kappa in the reliability analysis, a statistically significant (p < 0.001) moderate agreement (κ = 0.48) was found.
conclusionChatGPT outperformed Google by providing more accurate, well-referenced responses and utilizing more credible academic sources. This suggests its potential as a more reliable tool for obtaining health-related information.
Indexed as
Identifiers
40819320What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.