Increased Use of Generative Artificial Intelligence-Associated Language in Emergency Medicine Residency Personal Statements

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Background: Residency leaders increasingly rely on personal statements to select candidates.The availability of artificial intelligence (AI) writing tools raises concerns that personal statements may reflect AI-generated writing rather than authentic applicant voices.Objective: Assess the prevalence and impact of AIgenerated writing in EM residency personal statements submitted for the 2024 application cycle.Methods: This retrospective study analyzed personal statements submitted to the EM residency of a large academic medical center from 2017 to 2024.The primary outcome was the prevalence of 27 AI-associated target words identified in prior research, or 12 control words, compared between 2024 and 2017-2023 (pre-widespread release of AI writing tools) using one-sample t tests.Secondary outcomes included complexity (Flesch Reading Ease, word count), lexical diversity (type-token ratio), and personalization (first-and third-person pronoun frequency).Results: A total of 8,617 statements were studied (7,803 pre-2024, 814 in 2024).The proportion of statements with AI-associated words increased significantly from pre-2024 to 2024 (22.9% vs. 33.2%,P<0.001) (Figure 1).Control words were unchanged (84.4% vs. 84.3%,P=0.720).Words with the most significant absolute increases were "pivotal" (2.2% to 8.5%), "underscore" (0.5% to 3.9%), and "invaluable" (6.9% to 8.9%) (Figure 2).Word count decreased (686.5 vs. 674.3words, P=0.005).Flesch Reading Ease decreased (43.9 vs. 41.9,P<0.001) but remained at the college level.Type-token ratio increased (0.487 vs. 0.500, P<0.001), suggesting greater

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