• Int J Obstet Anesth · Nov 2024

    Artificial intelligence-created personal statements compared with applicant-written personal statements: a survey of obstetric anesthesia fellowship program directors in the United States.

    • A M Ruiz, M B Kraus, K W Arendt, D R Schroeder, and E E Sharpe.
    • Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States.
    • Int J Obstet Anesth. 2024 Nov 15; 61: 104293104293.

    BackgroundA personal statement is a common requirement in medical residency and fellowship applications. Generative artificial intelligence may be used to create a personal statement for these applications.MethodsTwo personal statements were created using OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) and two applicant-written statements were collected. A survey was sent to obstetric anesthesia fellowship program directors in the United States to assess the perceived readability, authenticity, and originality of the four personal statements. In addition, the survey assessed perceptions of applicants who use artificial intelligence to write a personal statement, including their integrity, work ethic, reliability, intelligence, and English proficiency.ResultsSurveyed fellowship directors could not accurately discern whether statements were applicant-written or artificial intelligence-generated. The artificial intelligence-generated personal statements were rated as more readable and original than the applicant-written statements. Most program directors were moderately or extremely concerned about the applicant's integrity, work ethic, and reliability if they suspected the applicant utilized ChatGPT.ConclusionsProgram directors could not accurately discern if the statements were written by a person or artificial intelligence and would have concerns about an applicant suspected of using artificial intelligence. Medical training programs may benefit from outlining their expectations regarding applicants' use of artificial intelligence.Copyright © 2024 Elsevier Ltd. All rights reserved.

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