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A Paradigm Shift In Integrated Plastic Surgery Residency Applications: One In Five Letters Of Recommendation Are Now Modified By Artificial Intelligence
Nikita O. Shulzhenko, MD1, Edward S. Lee, MD
1, Aditi M. Kanth, MD
2.
1Rutgers New Jersey Medical School, Newark, NJ, USA, 2Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
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). CONCLUSIONS: 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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