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Large Language Model Use in Radiology Residency Applications: Unwelcomed but Inevitable.

Emile B Gordon1, Charles M Maxfield2, Robert French2

  • 1Department of Radiology, Duke University Health System, Durham, North Carolina; Department of Radiology, University of California San Diego, La Jolla, California.

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Radiology program directors found large language model (LLM)-generated personal statements lower in quality. Despite concerns about authenticity, they recognized the increasing use of AI in residency applications.

Keywords:
ChatGPTLLMeducationmedical studentsresidency

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Radiology Residency Admissions

Background:

  • The use of artificial intelligence (AI), including large language models (LLMs), is rapidly increasing across various professional fields.
  • Residency applications in medicine, particularly in competitive fields like radiology, rely heavily on personal statements to assess applicant suitability.
  • There is a growing concern regarding the authenticity and quality of personal statements potentially generated or enhanced by AI tools.

Purpose of the Study:

  • To investigate the perspectives of radiology program directors on the impact of LLM-generated personal statements submitted by residency applicants.
  • To evaluate how AI-generated personal statements are perceived in terms of quality and authenticity compared to human-authored statements.

Main Methods:

  • A mixed-methods study involving eight radiology program directors.
  • Participants completed a survey and engaged in focus group discussions after reviewing anonymized personal statements (original and AI-generated using GPT-4).
  • Statements were assessed on writing quality (voice, clarity, engagement, organization) and perceived origin using a 5-point Likert scale.

Main Results:

  • AI-generated statements received lower quality ratings (56% average or worse) compared to human-authored statements (29% average or worse).
  • Reviewers reliably identified human-authored statements (95%) despite low confidence in distinguishing AI-generated content.
  • Focus groups revealed concerns about AI diminishing authenticity and the value of personal statements, with divided opinions on AI regulation.

Conclusions:

  • Radiology program directors perceive LLM-generated personal statements as inferior in quality, noting a potential loss of the applicant's unique voice.
  • While directors can reliably distinguish AI-generated from human-authored statements, they acknowledge the increasing prevalence and inevitability of AI use in application materials.
  • The findings highlight a need for further discussion on the ethical use and regulation of AI in medical residency admissions.