Evidence map›Paper›PMID 41451752›Full record

ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2026

Gemini 1.5 Flash provides the most reliable content while ChatGPT-4o offers the highest readability for patient education on meniscal tears.

Başar Burak Çakmur, Ali Can Koluman, Mehmet Utku Çiftçi, Ebru Aloğlu Çiftçi, Nezih Ziroğlu

Abstract readComparative Study
In one paragraph

Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

1 citing paper in PubMed.

  1. Trial
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.

Başar Burak ÇakmurDepartment of Orthopedics and Traumatology, Beylikduzu State Hospital, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-2720-1975
Ali Can KolumanDepartment of Orthopedics and Traumatology, Bakirkoy Dr. Sadi Konuk Training and Research, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-0191-3229
Mehmet Utku ÇiftçiDepartment of Orthopedics and Traumatology, Sultan Abdulhamid Han Training and Research Hospital, Istanbul, Turkey.ORCID https://orcid.org/0000-0001-5594-4138
Ebru Aloğlu ÇiftçiFaculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Istanbul Okan University, Istanbul, Turkey.ORCID https://orcid.org/0000-0003-2459-5080
Nezih ZiroğluVocational School of Health Services, Department of Orthopedic Prosthetics and Orthotics, Acıbadem Mehmet Ali Aydınlar University, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-2595-9459

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this study was to comparatively evaluate the responses generated by three advanced artificial intelligence (AI) models, ChatGPT-4o (OpenAI), Gemini 1.5 Flash (Google) and DeepSeek-V3, to frequently asked patient questions about meniscal tears in terms of reliability, usefulness, quality, and readability.

methodsResponses from three AI chatbots, ChatGPT-4o (OpenAI), Gemini 1.5 Flash (Google) and DeepSeek-V3 (DeepSeek AI), were evaluated for 20 common patient questions regarding meniscal tears. Three orthopaedic specialists independently scored reliability and usefulness on 7-point Likert scales and overall response quality using the 5-point Global Quality Scale. Readability was analysed with six established indices. Inter-rater agreement was examined with intraclass correlation coefficients (ICCs) and Fleiss' Kappa, while between-model differences were tested using Kruskal-Wallis and ANOVA with Bonferroni adjustment.

resultsGemini 1.5 Flash achieved the highest reliability, significantly outperforming both GPT-4o and DeepSeek-V3 (p = 0.001). While usefulness scores were broadly similar, Gemini was superior to DeepSeek-V3 (p = 0.045). Global Quality Scale scores did not differ significantly among models. In contrast, GPT-4o consistently provided the most readable content (p < 0.001). Inter-rater reliability was excellent across all evaluation domains (ICC > 0.9).

conclusionAll three AI models generated high-quality educational content regarding meniscal tears. Gemini 1.5 Flash demonstrated the highest reliability and usefulness, while GPT-4o provided significantly more readable responses. These findings highlight the trade-off between reliability and readability in AI-generated patient education materials and emphasise the importance of physician oversight to ensure safe, evidence-based integration of these tools into clinical practice. LEVEL OF EVIDENCE: Level V, observation-based, expert opinion-based, or in vitro/artificial intelligence model evaluation.

Indexed as

Artificial IntelligenceComprehensionPatient Education as TopicTibial Meniscus InjuriesGenerative Artificial IntelligenceHumansReproducibility of ResultsSurveys and QuestionnairesChatGPTDeepSeekGeminilarge language modelsmeniscal tearpatient education

Identifiers

PMID41451752
PMCPMC12948341

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.