ReviewCureus2026
AI-Augmented Mentorship in Orthopedic Surgery: A Conceptual Framework for Expanding Access for Underrepresented and Less-Resourced Students.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Orthopedic surgery remains one of the least diverse medical specialties in the United States, with women and racial/ethnic minorities being significantly underrepresented. Traditional mentorship models often depend on institutional prestige, geographic proximity, and professional networks, creating barriers for underrepresented minority students and trainees from lower-resourced institutions. Increasing residency competitiveness further highlights disparities in mentorship access. The aim of this study was to examine how artificial intelligence (AI) technologies may expand mentorship access and reduce disparities in orthopedic surgery training pathways. This conceptual framework paper narratively synthesizes published literature on mentorship disparities in surgical training, AI applications in medical education, and residency selection equity, identified through database searches (PubMed, ERIC, Google Scholar) using terms including "orthopaedic surgery", "mentorship", "artificial intelligence", "large language models", "residency diversity", and "underrepresented minorities", supplemented by hand-searching reference lists of key articles. Three AI-driven approaches are integrated: (1) large language model (LLM)-based virtual mentorship platforms, (2) residency data dashboards, and (3) social media analytics for network mapping. AI-augmented mentorship platforms may improve accessibility, scalability, and transparency compared with traditional mentorship models. LLM-based tools provide continuous access to educational support, while dashboards and social media analytics may reduce informational and networking barriers. However, these are proposed benefits extrapolated from adjacent fields; none have been empirically validated in an orthopedic surgery context. Risks including algorithmic bias, unequal technology access, and limited psychosocial support must be addressed. Equity-centered implementation, diverse training datasets, human oversight, and bias monitoring are essential prerequisites to ensure these technologies reduce rather than worsen disparities. AI technologies have the potential to broaden mentorship access in orthopedic surgery, though this remains unproven empirically and requires prospective validation before programmatic adoption. Strategic integration of AI-based mentorship tools may help complement, rather than replace, traditional human mentorship. Rigorous evaluation, equity-centered design, human oversight, and strong data governance are essential. Collaboration among orthopedic organizations, medical schools, and residency programs will be necessary to ensure equitable implementation.
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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 Socratic graph.