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The integration of artificial intelligence (AI) in medical school admissions offers transformative potential, but its responsible implementation is critical to ensure fairness and effectiveness. To guide institutions, this article provides practical implementation strategies for the 6 core principles of responsible AI outlined by the Association of American Medical Colleges. Moving from principle to practice, the analysis is grounded in a concrete case study: a hypothetical predictive assessment and summary (PAS) tool that combines quantitative scoring with qualitative, evidence-based summaries of application materials. Through the lens of implementing the PAS tool, the authors examine key challenges and actionable solutions across all 6 principles. The discussion includes balancing data-driven prediction with holistic understanding, incorporating human judgment and oversight, and ensuring transparency through explainable AI. Furthermore, the article addresses strategies to protect applicant data privacy, mitigate algorithmic bias through the use of representative datasets and fairness-aware techniques, and establish robust monitoring frameworks for continuous evaluation. By providing this actionable framework, the article aims to empower medical education institutions to leverage AI's capabilities, upholding their commitment to a fair, transparent, and mission-driven selection process that fosters a competent and representative future physician workforce.