Sudah, Suleiman Y.

Introduction: International medical graduates (IMGs) continue to play an important role in the US health care system, but little is known about their trends of matching into highly competitive residencies such as orthopedic surgery. The purpose of this study was to analyze temporal trends of IMG applicants matching into US orthopedic residency programs between 2008 and 2024 and compare them with US allopathic (MD) and osteopathic (DO) graduates. Methods: Orthopedic residency specific data for years 2008 to 2024 was obtained from the National Resident Match Program and Electronic Residency Application Service. Variables collected included total number of programs, total positions offered, number of applicants (MD, DO, and IMG), positions filled, and fill rate for each group. Trends were examined using simple linear regression modeling. Results: The number of orthopedic residency programs increased from 160 (2008) to 218 (2024), with total positions increasing from 636 (2008) to 916 (2024). The IMG fill rate decreased from 2.99% in 2008 to 0.87% in 2024 ( p < 0.01 for linear trend; β −0.069). This corresponds to an absolute number decrease of 19 IMGs in 2008 to 8 in 2024. More specifically, the proportion of US IMGs decreased from 0.94% to 0.66%, and the proportion of non-US IMGs decreased from 2.04% to 0.21%. Over the 17-year study period, a total of 105 US IMGs and 110 non-US IMGs matched into orthopedic surgery. The US MD applicant fill rate decreased significantly from 96.5% to 85.0% ( p < 0.001; β −0.944). The DO applicant fill rate increased significantly from 0.31% to 13.97% ( p < 0.0001; β 0.990). Conclusion: The declining rate of IMGs matching into orthopedic residency programs in the United States underscores the growing challenges faced by IMGs in accessing training in this competitive specialty. The notable increase in DO graduates securing orthopedic residency positions likely reflects the integration of a unified accreditation system for MD and DO residency programs established in 2020. Level of Evidence: Retrospective Cohort Study; IV

Purpose To evaluate the extent to which experienced reviewers can accurately discern between artificial intelligence (AI)–generated and original research abstracts published in the field of shoulder and elbow surgery and compare this with the performance of an AI detection tool. Methods Twenty‐five shoulder‐ and elbow‐related articles published in high‐impact journals in 2023 were randomly selected. ChatGPT was prompted with only the abstract title to create an AI‐generated version of each abstract. The resulting 50 abstracts were randomly distributed to and evaluated by 8 blinded peer reviewers with at least 5 years of experience. Reviewers were tasked with distinguishing between original and AI‐generated text. A Likert scale assessed reviewer confidence for each interpretation, and the primary reason guiding assessment of generated text was collected. AI output detector (0%‐100%) and plagiarism (0%‐100%) scores were evaluated using GPTZero. Results Reviewers correctly identified 62% of AI‐generated abstracts and misclassified 38% of original abstracts as being AI generated. GPTZero reported a significantly higher probability of AI output among generated abstracts (median, 56%; interquartile range [IQR], 51%‐77%) compared with original abstracts (median, 10%; IQR, 4%‐37%; P < .01). Generated abstracts scored significantly lower on the plagiarism detector (median, 7%; IQR, 5%‐14%) relative to original abstracts (median, 82%; IQR, 72%‐92%; P < .01). Correct identification of AI‐generated abstracts was predominately attributed to the presence of unrealistic data/values. The primary reason for misidentifying original abstracts as AI was attributed to writing style. Conclusions Experienced reviewers faced difficulties in distinguishing between human and AI‐generated research content within shoulder and elbow surgery. The presence of unrealistic data facilitated correct identification of AI abstracts, whereas misidentification of original abstracts was often ascribed to writing style. Clinical Relevance With rapidly increasing AI advancements, it is paramount that ethical standards of scientific reporting are upheld. It is therefore helpful to understand the ability of reviewers to identify AI‐generated content.