ArticleThe Laryngoscope2025
Generative AI in Otolaryngology Residency Personal Statement Writing: A Mixed-Methods Analysis.
Article in The Laryngoscope, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
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Who cites it
2 citing papers in PubMed.
- Benchmarking next-generation large language models in otorhinolaryngology: a prospective, guideline-anchored, blinded evaluation of ChatGPT-5, Gemini-3, Grok-2, Llama-4, and DeepSeek-4 across case scenarios.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 · 2026Article
- Generative AI in Otolaryngology Residency Personal Statement Writing: A Mixed-Methods Analysis.The Laryngoscope · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveGenerative Artificial Intelligence (GAI) interfaces have rapidly integrated into various societal domains. Widespread accessibility of GAI for drafting personal statements poses challenges for evaluators to gauge writing ability and personal insight. This study aims to compare the quality of GAI-generated personal statements to those written by successful applicants in OHNS residency programs, via integration of statistical and qualitative thematic analyses.
methodsPersonal statements were collected from successful OHNS residency applicants. Characteristic extraction from submitted statements was used to generate GAI-written personal statements using ChatGPT 4.0. All statements were blindly reviewed by 21 experienced evaluators on a 10-point Likert scale of authenticity, readability, personability, and overall quality. Thematic analysis of qualitative reviewer comments was conducted to extract deeper insights into evaluators' perceptions. Quantitative results were compared using independent t-tests, while thematic coding was performed inductively using NVivo software.
resultsGAI-generated personal statements significantly outperformed applicant-written statements in all assessed domains, including authenticity (7.67 vs. 7.05, p = 0.002), readability (8.03 vs. 7.49, p = 0.002), personability (7.33 vs. 6.72, p = 0.004), and overall score (7.49 vs. 6.90, p = 0.005). Thematic analysis revealed that GAI statements were seen as "well-constructed but generic," while applicant statements were often "verbose and lacked focus." Additionally, reviewers noted concerns regarding personal insight and engagement in AI-generated statements.
conclusionGAI-generated personal statements were rated more favorably across all domains, raising critical questions about the future of personal statements in the residency application process. While AI in medical education continues to evolve, clear guidelines on its ethical use in residency applications are essential. LEVEL OF EVIDENCE: N/A.
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Registered trials
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