ReviewATS scholar2024
Ethics of Writing Personal Statements and Letters of Recommendations with Large Language Models.
Review in ATS scholar, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Re-evaluating the role of personal statements in pediatric residency admissions in the era of artificial intelligence: comparing faculty ratings of human and AI-generated statements.Canadian medical education journal · 2025Article
- Can a Car Make a Blueberry Sandwich?Journal of graduate medical education · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models are becoming ubiquitous in the editing and generation of written content and are actively being explored for their use in medical education. The use of artificial intelligence (AI) engines to generate content in academic spaces is controversial and has been meet with swift responses and guidance from academic journals and publishers regarding the appropriate use or disclosure of use of AI engines in professional writing. To date, there is no guidance to applicants of graduate medical education programs in using AI engines to generate application content-primarily personal statements and letters of recommendation. In this Perspective, we review perceptions of using AI to generate application content, considerations for the impact of AI in holistic application review, ethical challenges regarding plagiarism, and AI text classifiers. Finally, included are recommendations to the graduate medical education community to provide guidance on use of AI engines in applications to maintain the integrity of the application process in graduate medical education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.