Evidence map›Paper›PMID 41249683›Full record

ArticleEuropean 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 Surgery2026

Comparative analysis of artificial intelligence platforms in generating Post-Operative instructions for endoscopic transnasal skull base surgery.

Ahlam H Alamri, Alya AlZabin, Nasir Magboul, Abdulaziz S Alrasheed, Ghassan Alokby, Ahmad Alroqi

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in 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. 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

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

1 citing paper in PubMed.

  1. Comment on 'comparative analysis of artificial intelligence platforms in generating post-operative instructions for endoscopic transnasal skull base surgery'.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 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ahlam H AlamriDepartment of Otolaryngology-Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia. ahlam.alamri@hotmail.com.ORCID http://orcid.org/0000-0002-2489-442X
Alya AlZabinCollege of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Nasir MagboulDepartment of Otolaryngology-Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Abdulaziz S AlrasheedDepartment of Otolaryngology-Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Ghassan AlokbyDepartment of Otorhinolaryngology Head & Neck Surgery, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia.
Ahmad AlroqiDepartment of Otolaryngology-Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeArtificial intelligence (AI) has emerged as a potential tool in postoperative care, particularly for complex procedures like Endoscopic Transnasal Skull Base Surgery (ETSBS), where patient comprehension of recovery instructions is critical. This study aimed to compare the readability, understandability, and actionability of postoperative instructions generated by three AI platforms (ChatGPT, DeepSeek, and Gemini).

methodsEach platform was prompted to create ETSBS postoperative instructions. Readability was assessed using Flesch Kincaid Grade Level (FKGL) and Reading Ease (FKRE). The Patient Education Materials Assessment Tool for printable materials (PEMAT-P) was used to evaluate understandability and actionability. Two outputs per platform were analyzed. Statistical comparisons were conducted using Kruskal-Wallis tests and Pearson correlation coefficients.

resultsGemini had the highest FKRE score (52.18), followed by DeepSeek (46.46) and ChatGPT (39.85), though differences were not significant (p = 0.458). FKGL was lowest in Gemini (9.07), compared to DeepSeek (9.82) and ChatGPT (10.87) (p = 0.469). Understandability scores were highest in ChatGPT and DeepSeek (76.45%), while Gemini scored lower (63.30%, p = 0.005). ChatGPT showed the highest actionability (58.5%), followed by DeepSeek (51.0%) and Gemini (45.15%), with no significant difference (p = 0.645). A strong inverse correlation was found between FKRE and FKGL (r = -0.998, p = 0.000). Correlations with understandability and actionability were moderate and non-significant (p > 0.1).

conclusionWhile AI platforms generated similarly readable content, significant differences emerged in usability. None met optimal standards for patient education, highlighting the need for clinician review before clinical application.

Indexed as

Artificial IntelligenceEndoscopyPatient Education as TopicPostoperative CareSkull BaseComprehensionHumansArtificial intelligenceChatGPTDeepSeekGeminiMachine learningOtolaryngologyPost-operative instructions

Identifiers

PMID41249683

What OpenQuestion holds

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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.