Artificial Intelligence vs Human Authorship in Spine Surgery Fellowship Personal Statements: Can ChatGPT Outperform Applicants?
Year: 2025
Type: article
Abstract: Study Design A comparative analysis of AI-generated vs human-authored personal statements for spine surgery fellowship applications. Objective To assess whether evaluators could differentiate between ChatGPT- and human-authored personal statements and determine if AI-generated statements could outperform human-authored ones in quality metrics. Summary of Background Data Personal statements are key in fellowship admissions, but the rise of AI tools like ChatGPT raises concerns about their use. While previous studies have examined AI-generated residency statements, their role in spine fellowship applications remains unexplored. Methods Nine personal statements (4 ChatGPT-generated, 5 human-authored) were evaluated by 8 blinded reviewers (6 attending spine surgeons and 2 fellows). ChatGPT-4o was prompted to create statements focused on 4 unique experiences. Evaluators rated each for readability, originality, quality, and authenticity (0-100 scale), determined AI authorship, and indicated interview recommendations. Results ChatGPT-authored statements scored higher in readability (65.69 vs 56.40, P = 0.016) and quality (63.00 vs 51.80, P = 0.004) but showed no differences in originality ( P = 0.339) or authenticity ( P = 0.256). Reviewers could not reliably distinguish AI from human authorship ( P = 1.000). Interview recommendations favored ChatGPT-generated statements (84.4% vs 62.5%, OR: 3.24 [1.08-11.17], P = 0.045). Conclusion ChatGPT can produce high quality, indistinguishable spine fellowship personal statements that increase interview likelihood. These findings highlight the need for nuanced guidelines regarding AI use in application processes, particularly considering its potential role in expanding access to high-quality writing assistance and editing.
Source: Global Spine Journal
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,650
OpenAlex ID: W4410534244