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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Academic Writing

Background:

  • Concerns exist regarding generative artificial intelligence (AI) use in residency personal statements.
  • Program directors may prefer human-generated content, prompting use of AI detection tools.
  • The accuracy of AI detection in personal statements remains uncertain.

Purpose of the Study:

  • To evaluate the accuracy of AI detection tools in identifying AI-generated content within residency personal statements.

Main Methods:

  • Collected 25 human-generated, 25 AI-generated (ChatGPT-4o), and 25 mixed-content personal statements.
  • Utilized four AI detection tools (free and paid) to assess statement likelihood of AI generation.
  • Performed statistical analyses, including multivariate analysis of variance (MANOVA) with post hoc tests.

Main Results:

  • AI detection tools showed variable likelihood scores for human-generated statements, with ranges up to 84%.
  • Significant differences in likelihood scores were found between statement types (P<.001).
  • A notable overlap existed between mixed-content and fully AI-generated statements.

Conclusions:

  • AI detection tools can inaccurately assign high AI likelihood scores to human-generated content.
  • Current AI detection tools are unreliable for distinguishing mixed-content from AI-generated personal statements.