Evidence map›Paper›PMID 38873173›Full record

ArticleJournal of experimental orthopaedics2024

Assessing the potential role of ChatGPT in spine surgery research.

Isabel Herzog, Dhruv Mendiratta, Ashok Para, Ari Berg, Neil Kaushal, Michael Vives

Abstract read
In one paragraph

Article in Journal of experimental orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Large language models in spine care and research.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Review
  2. Article
  3. Cautionary lessons from real-world testing of GPT-4.1 AI for pediatric foreign body aspiration.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
  4. Review
  5. Can LLMs simplify operative notes? A comparative analysis in otorhinolaryngology.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
  6. Article
  7. Assessing the potential role of ChatGPT in spine surgery research.Journal of experimental orthopaedics · 2024
    Article
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

6 authors.

Isabel HerzogRutgers New Jersey Medical School Newark New Jersey USA.ORCID https://orcid.org/0000-0001-7491-5746
Dhruv MendirattaRutgers New Jersey Medical School Newark New Jersey USA.
Ashok ParaRutgers New Jersey Medical School Newark New Jersey USA.
Ari BergRutgers New Jersey Medical School Newark New Jersey USA.
Neil KaushalRutgers New Jersey Medical School Newark New Jersey USA.
Michael VivesRutgers New Jersey Medical School Newark New Jersey USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Since its release in November 2022, Chat Generative Pre-Trained Transformer 3.5 (ChatGPT), a complex machine learning model, has garnered more than 100 million users worldwide. The aim of this study is to determine how well ChatGPT can generate novel systematic review ideas on topics within spine surgery. Methods: ChatGPT was instructed to give ten novel systematic review ideas for five popular topics in spine surgery literature: microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement. A comprehensive literature search was conducted in PubMed, CINAHL, EMBASE and Cochrane. The number of nonsystematic review articles and number of systematic review papers that had been published on each ChatGPT-generated idea were recorded. Results: Overall, ChatGPT had a 68% accuracy rate in creating novel systematic review ideas. More specifically, the accuracy rates were 80%, 80%, 40%, 70% and 70% for microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement, respectively. However, there was a 32% rate of ChatGPT generating ideas for which there were 0 nonsystematic review articles published. There was a 71.4%, 50%, 22.2%, 50%, 62.5% and 51.2% success rate of generating novel systematic review ideas, for which there were also nonsystematic reviews published, for microdiscectomy, laminectomy, spinal fusion, kyphoplasty, disc replacement and overall, respectively. Conclusions: ChatGPT generated novel systematic review ideas at an overall rate of 68%. ChatGPT can help identify knowledge gaps in spine research that warrant further investigation, when used under supervision of an experienced spine specialist. This technology can be erroneous and lacks intrinsic logic; so, it should never be used in isolation. Level of Evidence: Not applicable.

Indexed as

artificial intelligenceChatGPTmachine learningsystematic reviews

Identifiers

PMID38873173
PMCPMC11170336

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.