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Human reviewers struggled to distinguish AI-generated personal statements (PS) from human-written ones, impacting residency recruitment. This difficulty highlights the need to re-evaluate application processes as artificial intelligence becomes more prevalent.

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Area of Science:

  • Artificial Intelligence in Medical Education
  • Natural Language Processing in Healthcare Applications
  • Surgical Residency Admissions

Background:

  • Artificial intelligence (AI) algorithms can now generate human-like text, posing challenges in academic and professional settings.
  • Personal statements (PS) are a critical component of the surgical residency application process, offering insight into applicant qualities beyond objective metrics.
  • The increasing sophistication of AI text generation necessitates an evaluation of its impact on traditional application review methods.

Purpose of the Study:

  • To assess the capability of human reviewers to accurately differentiate between personal statements (PS) authored by humans and those generated by artificial intelligence (AI) software.
  • To evaluate the implications of AI-generated text on the residency selection process, specifically concerning interview offers.

Main Methods:

  • Four human-authored personal statements (PS) from surgical residency program archives were used as samples.
  • Two AI platforms generated nine additional PS, creating a dataset for evaluation.
  • Four experienced surgeons serving on residency selection committees acted as blinded reviewers to identify authorship, with AI also evaluating authorship.

Main Results:

  • Human reviewers demonstrated a sensitivity of 0.87 but a low specificity of 0.37, resulting in an overall accuracy of 0.55 in identifying AI-generated PS.
  • Inter-rater reliability among reviewers was poor (kappa = 0.067), indicating significant disagreement in authorship attribution.
  • Perceived human authorship significantly increased the odds of receiving an interview offer (OR=7, p=0.0144), underscoring the bias introduced by perceived authorship.

Conclusions:

  • Distinguishing AI-generated personal statements from human-written ones is exceedingly challenging for expert reviewers.
  • The pervasive use of AI in generating application materials may necessitate a fundamental review and potential restructuring of the resident recruitment and selection process.
  • As objective applicant data becomes scarcer, the reliance on and interpretation of personal statements in admissions requires careful consideration in the age of AI.