Evidence map›Paper›PMID 38769125›Full record

ArticleEuropean journal of orthopaedic surgery & traumatology : orthopedie traumatologie2024

Educating patients on osteoporosis and bone health: Can "ChatGPT" provide high-quality content?

Diane Ghanem, Henry Shu, Victoria Bergstein, Majd Marrache, Andra Love, Alice Hughes, Rachel Sotsky, Babar Shafiq

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Article in European journal of orthopaedic surgery & traumatology : orthopedie traumatologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
  2. OsteoCHAT: real-world patient evaluation and benchmarking of a guideline-grounded osteoporosis chatbot.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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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

8 authors.

Diane GhanemDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD, 21287, USA. dghanem1@jh.edu.ORCID http://orcid.org/0000-0002-5894-3373
Henry ShuSchool of Medicine, The Johns Hopkins University, Baltimore, MD, USA.
Victoria BergsteinSchool of Medicine, The Johns Hopkins University, Baltimore, MD, USA.
Majd MarracheDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD, 21287, USA.
Andra LoveDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD, 21287, USA.
Alice HughesDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD, 21287, USA.
Rachel SotskyDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD, 21287, USA.
Babar ShafiqDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD, 21287, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe rise of artificial intelligence (AI) models like ChatGPT offers potential for varied applications, including patient education in healthcare. With gaps in osteoporosis and bone health knowledge and adherence to prevention and treatment, this study aims to evaluate the accuracy of ChatGPT in delivering evidence-based information related to osteoporosis.

methodsTwenty of the most common frequently asked questions (FAQs) related to osteoporosis were subcategorized into diagnosis, diagnostic method, risk factors, and treatment and prevention. These FAQs were sourced online and inputted into ChatGPT-3.5. Three orthopedic surgeons and one advanced practice provider who routinely treat patients with fragility fractures independently reviewed the ChatGPT-generated answers, grading them on a scale from 0 (harmful) to 4 (excellent). Mean response accuracy scores were calculated. To compare the variance of the means across the four categories, a one-way analysis of variance (ANOVA) was used.

resultsChatGPT displayed an overall mean accuracy score of 91%. Its responses were graded as "accurate requiring minimal clarification" or "excellent," with a mean response score ranging from 3.25 to 4. No answers were deemed inaccurate or harmful. No significant difference was observed in the means of responses across the defined categories.

conclusionChatGPT-3.5 provided high-quality educational content. It showcased a high degree of accuracy in addressing osteoporosis-related questions, aligning closely with expert opinions and current literature, with structured and inclusive answers. However, while AI models can enhance patient information accessibility, they should be used as an adjunct rather than a substitute for human expertise and clinical judgment.

Indexed as

OsteoporosisPatient Education as TopicArtificial IntelligenceHumansOsteoporotic FracturesArtificial IntelligenceBone HealthChatGPTLanguage ModelOsteoporosisPatient Education

Identifiers

PMID38769125

What OpenQuestion holds

Textmetadata
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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.