P57. A Paradigm Shift in Integrated Plastic Surgery Residency Applications: One in Five Letters of Recommendation Are Now Modified by Artificial Intelligence

Abstract

PURPOSE: Transformations such as pass/fail grading, novel curriculums, and publication inflation have placed greater emphasis on holistic factors in resident selection. Sub-internship performance and letters of recommendation (LORs) often serve as relationally-focused differentiating criteria. The prevalence and influence of artificial intelligence (AI) tools in authoring such LORs are wholly uncharacterized. METHODS: Following PSCA permission, LORs for the 2024-2025 Integrated Plastic Surgery residency match were collected alongside relevant author and applicant information. LOR body text was processed through lenient and strict algorithms from validated commercial AI-detection software. The study was self-funded and data retention was explicitly denied for corporate and/or model-training PURPOSEs. RESULTS: 1,413 LORs from all 381 applications were evaluated. Most authors were plastic surgeons (90.4%) with academic practices (96.5%). Program directors authored 32.8% and chairs/chiefs authored 42.4% of letters. Multiple authors were noted for 268 (18.9%) letters. Lenient and strict algorithms both agreed with >95% confidence on 1,109 letters, identifying 238 (21.5%) letters as AI-assisted. AI-assisted letters were significantly longer (494±156 vs. 388±147 words, p<.001). On multivariate analysis, multiple-authorship was a significant predictor of AI-use (OR=1.48, p<0.05), with chair/chief-authorship approaching significance (OR=1.35, p=0.06). Reapplicant-status, author-specialty, practice-setting, relationship-length, applicant-ranking, and program-director-authorship did not predict AI-use (all p>0.10). CONCLUSION: We present the contemporary cohort of Integrated Plastic Surgery letter of recommendation authors, and conservatively demonstrate that over 20% likely used AI-assisted tools. While the integrity and implications of AI-use in academic promotion are unresolved, this insight calls for transparent AI guidelines and questions if traditional metrics in residency selection need recalibration.

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