ArticleThe Laryngoscope2025
Preparing for AI in Resident Selection: A Scoping Review of Current Applications and Limitations.
Article in The Laryngoscope, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Characteristics and Success of Internal Medicine Applicants to Subspecialty Fellowships.Journal of general internal medicine · 2026Article
- Artificial Intelligence in Psychiatric Graduate Medical Education.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Tackling Administrative Burden in Graduate Medical Education: Consider AI-Powered Tools.Journal of graduate medical education · 2026Article
- Preparing for AI in Resident Selection: A Scoping Review of Current Applications and Limitations.The Laryngoscope · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesResidency programs face a large number of applications with limited time to review them. Artificial intelligence (AI) tools could potentially streamline this process. This scoping review examines how AI has been applied in the resident selection process, the impacts described, and the limitations identified. DATA SOURCES: PubMed, OVID, Cochrane, EMBASE, Google Scholar, and Web of Science. REVIEW
methodsA comprehensive search strategy was developed using keywords related to artificial intelligence, natural language processing, machine learning, medical residency, and personnel selection. Studies were included if they were published in English in the last 10 years, involved real or simulated medical or surgical residency applicants or applications, and used AI in any part of the selection process. Articles were identified and screened independently by two reviewers, with conflicts resolved by consensus.
resultsOf 115 identified articles, 11 met inclusion criteria. Applications of AI fell into three thematic areas: assessing bias (n = 4), predicting selection outcomes (n = 5), and comparing AI to human reviewers (n = 2). AI models included natural language processing, machine learning, and large language models. While several studies demonstrated AI's potential to detect bias, improve reviewer consistency, and identify overlooked applicants, concerns remain about bias replication, limited generalizability, and the interpretability of AI-driven decisions.
conclusionsArtificial intelligence holds promise for a more efficient and equitable resident selection process. Further investigation is needed to guide responsible development, validation, and oversight of these tools.
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.