Evidence map›Paper›PMID 38248805›Full record

ArticleJournal of personalized medicine2024

Assessment of Quality and Readability of Information Provided by ChatGPT in Relation to Anterior Cruciate Ligament Injury.

Stephen Fahy, Stephan Oehme, Danko Milinkovic, Tobias Jung, Benjamin Bartek

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–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

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Quality of AI chatbot-generated information on hypersensitivity pneumonitis for clinical and patient use.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2026
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  12. Article
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  14. Review
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  16. Article
  17. A custom ChatGPT can accurately answer questions from an international expert osteotomy consensus statement.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025
    Article
  18. Article
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  20. 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

5 authors.

Stephen FahyCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.ORCID 0000-0002-4503-1842
Stephan OehmeCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.ORCID 0000-0001-5044-2657
Danko MilinkovicCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.
Tobias JungCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.
Benjamin BartekCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of our study was to evaluate the potential role of Artificial Intelligence tools like ChatGPT in patient education. To do this, we assessed both the quality and readability of information provided by ChatGPT 3.5 and 4 in relation to Anterior Cruciate Ligament (ACL) injury and treatment. ChatGPT 3.5 and 4 were used to answer common patient queries relating to ACL injuries and treatment. The quality of the information was assessed using the DISCERN criteria. Readability was assessed with the use of seven readability formulae: the Flesch-Kincaid Reading Grade Level, the Flesch Reading Ease Score, the Raygor Estimate, the SMOG, the Fry, the FORCAST, and the Gunning Fog. The mean reading grade level (RGL) was compared with the recommended 8th-grade reading level, the mean RGL among adults in America. The perceived quality and mean RGL of answers given by both ChatGPT 3.5 and 4 was also compared. Both ChatGPT 3.5 and 4 yielded DISCERN scores suggesting "good" quality of information, with ChatGPT 4 slightly outperforming 3.5. However, readability levels for both versions significantly exceeded the average 8th-grade reading level for American patients. ChatGPT 3.5 had a mean RGL of 18.08, while the mean RGL of ChatGPT 4 was 17.9, exceeding the average American reading grade level by 10.08 grade levels and 9.09 grade levels, respectively. While ChatGPT can provide both reliable and good quality information on ACL injuries and treatment options, the readability of the content may limit its utility. Additionally, the consistent lack of source citation represents a significant area of concern for patients and clinicians alike. If AI is to play a role in patient education, it must reliably produce information which is accurate, easily comprehensible, and clearly sourced.

Indexed as

ACL reconstruction surgery (ACL-R)anterior cruciate ligament (ACL)artificial intelligence (AI)ChatGPTDISCERN criteriahealth literacynatural language processingorthopaedic injuriespatient education materials (PEMS)readability

Identifiers

PMID38248805
PMCPMC10817257

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.