Access to medical education remains disproportionately limited for students from low-income households and groups underrepresented in medicine (URiM). Although many pipeline programs exist, no unifying, reproducible framework guides their development, implementation, or evaluation. To address this, the Linking Institutions and Neighborhoods for Knowledge in Medicine (LINK-Med) framework was developed as a four-phase stepwise model for building partnerships between medical schools, clinical institutions, and K–12 educational systems. LINK-Med was applied at Brown University during the 2023–2024 academic year through the Clinical Mentorship Program (CMP), a student-led initiative providing longitudinal mentorship and clinical exposure to high school students in Woonsocket, Rhode Island. The program enrolled 27 high school students and was implemented using existing institutional resources without dedicated external funding. Application of LINK-Med enabled rapid partnership development and programming across a full academic year. Detailed program evaluation findings are reported separately. Future work will focus on expanding LINK-Med across additional sites, developing standardized evaluation instruments, and assessing long-term outcomes for participating students. LINK-Med offers a scalable, low-cost model for increasing access to early medical education. While pathway programs are increasingly recognized as essential for expanding access to medicine, yet institutions lack a blueprint for creating them. LINK-Med introduces a novel implementation framework and geographic feasibility metric that help medical schools build sustainable partnerships with hospitals and local schools. This scalable model transforms successful local initiatives into a reproducible approach for broader adoption.

Students from low-income and underrepresented backgrounds face structural barriers to entering medicine, including limited access to early clinical exposure, physician guidance, and professional networks. To address these disparities, medical students at the Warren Alpert Medical School of Brown University created a year-long Clinical Mentorship Program (CMP) for high school students in Woonsocket, Rhode Island. The intervention combined monthly after-school instruction on basic science content and clinical skills by medical students with structured hospital-based shadowing and guided discussion with resident physicians. Using a mixed-methods pre-post design, the intervention evaluated changes in educational motivation, clinical self-efficacy, medical knowledge, awareness of other academic enrichment programs, and physician access and comfort. High school students demonstrated significant gains in motivation to attend college, understanding of cardiopulmonary anatomy and physiology, understanding of physician roles, comfort interacting with physicians, self-reported physician access, and awareness of local educational opportunities. Qualitative findings highlighted the importance of experiential learning, guided shadowing, a clearer understanding of medical careers, and persistent structural barriers. These findings suggest that community-based programs that leverage local resources, networks, and infrastructure to combine medical education with guided clinical exposure may be feasible entry points into the medical pipeline for students confronting complex socioeconomic disadvantages.

Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students, yet AI curricula and competency recommendations for undergraduate medical education (UME) remain fragmented. We conducted a PRISMA-ScR scoping review to map and synthesize proposed AI competencies for UME by performing a global search of PubMed, Embase, Web of Science, and ERIC without language restrictions and from database inception through July 28, 2025. Verbatim competency-relevant text was extracted and decomposed into discrete statements and classified using domains, competencies, or learning objectives. Statement frequencies were summarized to characterize recurring areas of emphasis, underrepresented topics, and cross-domain relationships. Of 4071 records identified, 54 studies from 22 countries met inclusion criteria. From 564 eligible statements, we synthesized a taxonomy comprising seven domains (AI ethics; AI law and regulation; AI professionalism in healthcare; clinical applications of AI; critical appraisal of AI output; research and innovation in AI; theory and foundations of AI) spanning 37 competencies and 170 learning objectives. Sources were predominantly editorial/opinion, with recurring emphasis on ethicolegal oversight, critical appraisal of AI outputs, and foundational understanding of AI methods and data. This synthesis provides a structured inventory to inform curriculum planning and future stakeholder-based refinement, prioritization, and evaluation.

How do related, but independent community colleges operationalize and implement an equity-oriented initiative? In 2021, Massachusetts established the SUCCESS initiative to provide nonfinancial student supports in all 15 of the state's public community colleges. This paper draws on 35 interviews with program staff across the colleges to ask how they used their institutional data and designed new data collections to (1) select a focal population for their SUCCESS program and (2) apply their criteria to identify a specific cohort. While we find variation in what the colleges viewed as their most urgent equity gaps, we find that in all colleges, data availability informed how practitioners could identify their focal students. Our findings illustrate how diverse community colleges within one state respond differently to the same equity-oriented initiative; they operationalize equity in different ways that are related to their institutional priorities and data availability.