José A. Acosta MD, MBA, MPH

Ponce Health Sciences University St. Louis Campus

Introduction 

The rapid integration of artificial intelligence (AI) into medicine is reshaping clinical practice, research, and medical education. Large language models (LLMs) such as ChatGPT, Claude, and Bard have demonstrated capabilities in passing medical licensing exams, assisting with clinical decision-making, and enhancing medical training. However, despite the widespread adoption of AI tools among medical students, most receive little or no formal training on their responsible and effective use. Without structured AI literacy in medical education, students risk over-reliance on these technologies, misinterpretation of AI-generated outputs, and failure to recognize AI’s inherent limitations and biases. To equip future physicians with the competencies needed in an AI-driven healthcare landscape, medical schools must integrate AI literacy—including prompt engineering, critical evaluation of AI outputs, and ethical considerations—into their curricula immediately.

AI Is Already Reshaping Medical Education 

AI tools are already widely used by medical students for studying, research, and clinical training. A recent cross-sectional study of medical students in Uganda found that over 75% of students regularly use AI tools such as ChatGPT, Bing AI, and Bard, primarily for completing assignments, preparing for tutorials, and studying for exams.¹ Notably, students also reported using AI for non-traditional purposes, including emotional support, counseling, and personal productivity. AI-driven tools are being increasingly adopted to enhance curriculum development, assessment design, and student learning experiences, reinforcing the need for structured AI education in medical training.²

Beyond general study assistance, AI-generated virtual patients are transforming clinical training. Recent research has demonstrated that LLM-driven systems can enhance medical education by providing real-time feedback, adapting to learners’ inputs, and assisting in clinical reasoning. While LLMs have been used to generate simulated patient cases and evaluate student interactions, their primary role in medical training has focused on offering structured, adaptive guidance rather than fully interactive virtual patient simulations.³ This technology offers scalable, low-cost solutions for medical education, particularly in resource-limited settings. However, without formal instruction, students may struggle to critically assess AI-generated content, distinguish between high- and low-quality AI outputs, and avoid confirmation bias when using AI for clinical decision-making.⁴

Why AI Literacy Must Be a Core Competency in Medical Education 

Despite the growing reliance on AI, many medical schools lack structured AI curricula, leaving students to navigate these tools without guidance. A structured, progressive approach has been piloted successfully in nursing education, where AI literacy was integrated across three academic years, focusing on foundational knowledge, applied learning in patient education, and AI-assisted critical thinking.⁵ A similar approach should be adopted in medical education to ensure that students develop AI proficiency in a scaffolded manner.

The Algorithmic Literacy Framework (ALF) provides a model for medical schools to assess AI readiness at both institutional and individual levels.⁶ ALF identifies key areas for AI integration, including faculty and student engagement, technical and ethical considerations of AI use in medicine, and interdisciplinary collaboration between medical and computer science departments.

Medical education must go beyond merely introducing students to AI tools and instead equip them with the skills to critically engage with AI. The first essential competency in AI literacy is prompt engineering, where students learn how to craft precise AI queries to elicit accurate, relevant, and clinically sound responses. In addition, students must be trained in the critical evaluation of AI outputs, ensuring that they can identify misinformation—AI-generated falsehoods—and assess AI-generated content against evidence-based medicine. Finally, ethical and legal considerations surrounding AI use must be incorporated into medical curricula, including AI bias, patient data privacy, and regulatory frameworks, to ensure future physicians understand the broader implications of AI-driven healthcare.

Addressing Barriers and Challenges 

One of the key barriers to integrating AI into medical education is faculty hesitation. Many educators express concerns about academic integrity, AI’s potential to replace human instructors, and a lack of institutional guidance.⁷ However, AI should be positioned as a complementary tool rather than a replacement for human instruction. Faculty development programs should be introduced to equip educators with the skills to incorporate AI into medical training responsibly.

Beyond faculty concerns, access to AI tools remains inequitable across institutions. Many students in low-resource settings rely on free versions of AI models, which may lack the accuracy and updated medical knowledge of premium AI tools. Without equitable access to AI education, disparities in AI competency may emerge among future physicians. Institutions must ensure that all students, regardless of their geographic or financial circumstances, have access to AI training and resources.

Additionally, AI’s role in assessments must be reconsidered. Traditional evaluation methods, such as essay-based exams and take-home assignments, may need to be adapted to reflect AI’s increasing presence in students’ workflows. Medical educators should design assessments that evaluate higher-order thinking skills, requiring students to apply AI outputs critically rather than passively accepting AI-generated responses.

Conclusion: The Future of AI in Medical Education 

The widespread, unstructured use of AI among medical students is an urgent call to action. AI is already a permanent fixture in medical education, yet most students are using it without formal training, risking misuse, over-reliance, and the propagation of inaccurate medical information.

Medical schools must act now to integrate AI literacy into their curricula, ensuring students develop competencies in AI-assisted decision-making, prompt engineering, and ethical considerations. AI is not the future, it is the present, and the next generation of physicians must be prepared to engage with it responsibly.

This article is human authored to ensure compliance with copyright law, with AI (OpenAI 2025 ChatGPT 4.0) used solely as an assistive tool for research and drafting support. All sources and claims have been independently verified against authoritative, peer-reviewed literature, maintaining the integrity and originality required for copyright transfer upon acceptance.

References 

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5.Ganjavi C, Eppler M, O’Brien D, et al. ChatGPT and large language models (LLMs) awareness and use. A prospective cross-sectional survey of U.S. medical students. PLOS Digital Health. 2024;3(9):e0000596. doi:10.1371/journal.pdig.0000596

6.Frehywot S, Vovides Y. Contextualizing algorithmic literacy framework for global health workforce education. Artificial Intelligence in Health. 2024:4903.

7.Nguyen LT, Tran VT, Tran JT, Oorjitham NS. Taught by a Robot: A Trainee Perspective on Artificial Intelligence in Medical School Education. Academic Psychiatry. 2024/11/11 2024;doi:10.1007/s40596-024-02081-8