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AI-Augmented Mentorship in Orthopedic Surgery: A Conceptual Framework for Expanding Access for Underrepresented and
Joseph Salem-Hernández1, Peter A Santiago-Gadea2, Camilla A Pérez-Vicente2
1Department of Orthopedic Surgery, Ponce Health Sciences University, Ponce, PRI.
Cureus
|July 14, 2026
Summary
Artificial intelligence (AI) may enhance mentorship access in orthopedic surgery, addressing underrepresentation. While promising, AI tools require validation and careful implementation to avoid bias and ensure equitable access for diverse trainees.
Area of Science:
- Medical Education
- Surgical Training
- Health Equity
Background:
- Orthopedic surgery exhibits significant underrepresentation of women and racial/ethnic minorities in the US.
- Traditional mentorship models create barriers for underrepresented minorities due to reliance on institutional prestige, geography, and networks.
- Increasing residency competitiveness exacerbates existing disparities in mentorship access.
Purpose of the Study:
- To explore the potential of artificial intelligence (AI) technologies to expand mentorship access and reduce disparities in orthopedic surgery training.
- To examine AI-driven approaches for improving equity in medical education and residency selection.
Main Methods:
- A narrative synthesis of literature on mentorship disparities, AI in medical education, and residency selection equity.
- Searches of PubMed, ERIC, and Google Scholar using keywords like "orthopaedic surgery," "mentorship," "artificial intelligence," and "underrepresented minorities."
- Integration of three AI-driven approaches: LLM-based virtual mentorship, residency data dashboards, and social media analytics for network mapping.
Main Results:
- AI-augmented mentorship platforms offer potential improvements in accessibility, scalability, and transparency over traditional models.
- Large language model (LLM)-based tools can provide continuous educational support.
- Residency data dashboards and social media analytics may mitigate informational and networking barriers.
Conclusions:
- AI technologies show potential for broadening mentorship access in orthopedic surgery, but empirical validation is needed.
- Risks such as algorithmic bias and unequal technology access must be addressed through equity-centered implementation.
- AI tools should complement, not replace, traditional human mentorship, requiring rigorous evaluation, bias monitoring, and collaboration for equitable adoption.
Keywords:
artificial intelligencediversityhealth equitylarge language modelsmedical educationmentorshiporthopaedic surgeryresidency trainingsocial mediaunderrepresented minorities
