Evidence map›Paper›PMID 40791971›Full record

ArticleJournal of the Pediatric Orthopaedic Society of North America2025

Artificial Intelligence-Based Large Language Models Can Facilitate Patient Education.

Xochitl Bryson, Marleni Albarran, Nicole Pham, Arianne Salunga, Taylor Johnson, Grant D Hogue, Jaysson T Brooks, Kali R Tileston, Craig R Louer, Ron El-Hawary and 9 more

Abstract read
In one paragraph

Article in Journal of the Pediatric Orthopaedic Society of North America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Xochitl BrysonStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Marleni AlbarranStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Nicole PhamStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Arianne SalungaStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Taylor JohnsonStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Grant D HogueBoston Children's Hospital, Boston, MA, USA.
Jaysson T BrooksTexas Scottish Rite Hospital for Children, Dallas, TX, USA.
Kali R TilestonStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Craig R LouerDepartment of Orthopaedics and Rehabilitation, Vanderbilt University Medical Center, Nashville, TN, USA.
Ron El-HawaryBoston Children's Hospital, Boston, MA, USA.
Meghan N ImrieStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
James F PolicyStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.
Daniel BoutonShriners Hospital for Children, Portland, OR, USA.
Arun R HariharanPaley Orthopedic and Spine Institute, West Palm Beach, FL, USA.
Sara Van NortwickMedical University of South Carolina, Charleston, SC, USA.
Vidyadhar V UpasaniUniversity of California, San Diego - Department of Orthopaedic Surgery, San Diego, CA, USA.
Jennifer M BauerSeattle Children's Hospital, Seattle, WA, USA.
Andrew TiceChildren's Hospital of Eastern Ontario, Ottawa, Ontario, Canada.
John S VorhiesStanford School of Medicine Department of Orthopedic Surgery, Palo Alto, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) large language models (LLMs) are becoming increasingly popular, with patients and families more likely to utilize LLM when conducting internet-based research about scoliosis. For this reason, it is vital to understand the abilities and limitations of this technology in disseminating accurate medical information. We used an expert panel to compare LLM-generated and professional society-authored answers to frequently asked questions about pediatric scoliosis. Methods: We used three publicly available LLMs to generate answers to 15 frequently asked questions (FAQs) regarding pediatric scoliosis. The FAQs were derived from the Scoliosis Research Society, the American Academy of Orthopaedic Surgeons, and the Pediatric Spine Foundation. We gave minimal training to the LLM other than specifying the response length and requesting answers at a 5th-grade reading level. A 15-question survey was distributed to an expert panel composed of pediatric spine surgeons. To determine readability, responses were inputted into an open-source calculator. The panel members were presented with an AI and a physician-generated response to a FAQ and asked to select which they preferred. They were then asked to individually grade the accuracy of responses on a Likert scale. Results: The panel members had a mean of 8.9 years of experience post-fellowship (range: 3-23 years). The panel reported nearly equivalent agreement between AI-generated and physician-generated answers. The expert panel favored professional society-written responses for 40% of questions, AI for 40%, ranked responses equally good for 13%, and saw a tie between AI and "equally good" for 7%. For two professional society-generated and three AI-generated responses, the error bars of the expert panel mean score for accuracy and appropriateness fell below neutral, indicating a lack of consensus and mixed opinions with the response. Conclusions: Based on the expert panel review, AI delivered accurate and appropriate answers as frequently as professional society-authored FAQ answers from professional society websites. AI and professional society websites were equally likely to generate answers with which the expert panel disagreed. Key Concepts: (1)Large language models (LLMs) are increasingly used for generating medical information online, necessitating an evaluation of their accuracy and effectiveness compared with traditional sources.(2)An expert panel of physicians compared artificial intelligence (AI)-generated answers with professional society-authored answers to pediatric scoliosis frequently asked questions, finding that both types of answers were equally favored in terms of accuracy and appropriateness.(3)The panel reported a similar rate of disagreement with AI-generated and professional society-generated answers, indicating that both had areas of controversy.(4)Over half of the expert panel members felt they could distinguish between AI-generated and professional society-generated answers but this did not relate to their preferences.(5)While AI can support medical information dissemination, further research and improvements are needed to address its limitations and ensure high-quality, accessible patient education. Levels of Evidence: IV.

Indexed as

Artificial intelligenceEducationLarge language modelsOrthopaedicsScoliosis

Identifiers

PMID40791971
PMCPMC12337203

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.