<p>The use of artificial intelligence (AI) in medical school application processes is gaining traction. Machine learning, natural language processing (NLP), and large language models (LLM) are all tools being used to analyze both structured and unstructured aspects of medical school and residency program applications. Many studies have outlined the benefits of this approach, namely in reducing burden on admissions committees, reducing bias, and improving efficiency. However, little attention has been paid to the student perspective on this integration of AI. As students, we have concerns with the use of AI in this process due to potential limitations in appreciating emotional nuance, the inconsistency of generative AI, the possibility of perpetuating existing bias, and the opportunity for students to take advantage of systemic loopholes. Although we hold these concerns, we also recognize the benefits that could result from the implementation of AI. As such, we believe that a hybrid model that leverages the strengths of both human reviewers and AI is the most responsible and optimal path forward. A method that uses AI as a first-pass tool and leaves the final decision to human evaluators would promote efficiency and preserve the essential role that human judgement plays in the admissions process. Prioritizing transparency throughout will help reduce concerns that students, like ourselves, may have. The future of medicine depends on an admissions process that is able to select compassionate and empathetic future physicians in an efficient and fair manner.</p>