Integrating artificial intelligence into pre-clinical medical education: challenges, opportunities, and recommendations
Field: EDUCATION

As AI technologies continue to advance and influence healthcare, it is imperative that medical education evolves to equip future physicians with the necessary skills and understanding of AI applications. In pre-clinical curricula, AI tools can streamline administrative processes, enhance teaching methodologies, and provide personalized learning experiences for students. Moreover, AI has the potential to shape students' professionalism, ethical decision-making, and critical thinking skills, which are essential for their future roles in the medical field. However, this shift also raises critical challenges, such as the need for ethical guidelines, adequate infrastructure, and avoiding overreliance on AI. Recent studies have shown a significant gap in formal AI education within medical studies, while the general attitudes of students towards AI is positive [1] This paper is based on a comprehensive narrative review of current literature and expert discussions. It aims to identify the key areas affected by AI integration in pre-clinical medical education, and to provide recommendations to maximize its benefits while mitigating associated risks.Universities face significant organizational challenges in integrating AI, such as ensuring ethical AI usage, addressing data privacy concerns, and meeting infrastructural requirements. Developing comprehensive policies to guide AI's role in academic settings is essential, including its application in exams, theses, teaching, and clinical decision-making tools [1][4]. Policy development requires good cooperation between academic self-governance bodies (academic freedom) and university management (management of personnel and financial resources, and enforcement of rules) [2].One significant opportunity presented by AI integration is the optimization of administrative tasks. AI-driven data analysis can improve resource management, allowing universities to allocate resources more efficiently and effectively [5][6]. Additionally, AI can enhance global collaboration in medical education by facilitating communication and information sharing across institutions worldwide. Implementing digital learning infrastructures, such as virtual simulation labs and AIassisted learning platforms, has the potential to improve teaching efficiency and provide innovative educational experiences for students [7][8].However, several challenges persist. Universities must invest in digital technologies while balancing traditional educational needs to prevent over-reliance on AI [8][9]. Ensuring equal and fair access to AI tools for all students is crucial to avoid disparities in educational opportunities. The freedom of teaching should not be impaired by university-wide adjustments. Establishing ethical frameworks is imperative to promote the responsible use of AI, especially concerning patient data in AI models [8][10] [11][12]. Addressing these ethical considerations is essential for safeguarding data privacy and upholding academic integrity.At the program management level, aligning the needs of students and educators regarding AI usage in education is crucial. This alignment impacts curriculum design, coordination among educators, and the preparation of students for future job roles in an AI-influenced healthcare environment [13].One of the primary challenges is ensuring that curricula remain relevant amid AI's growing impact on healthcare. This necessitates significant restructuring of courses to integrate AI tools while maintaining the essential human elements of medical education, such as patient interaction and ethical decision-making [10] [14][15] [16]. Program managers must balance the incorporation of new technologies with the preservation of core medical competencies to provide a comprehensive education under conditions in which these core competencies are continually reassessedConversely, AI offers opportunities to develop innovative course structures that include experiential components. For instance, AI simulations for clinical decision-making can enhance learning outcomes by providing students with practical, hands-on experience in a controlled environment. Additionally, AI can help identify future job market needs, enabling study programs to adapt their curricula and train students in AI-driven medical technologies [1][6][17]. This proactive approach ensures that graduates are better prepared for the evolving demands of the healthcare sector.For teaching staff, integrating AI into medical curricula demands the acquisition of new skills and methodologies, as well as an understanding of how to balance AI with traditional teaching methods. AI tools such as virtual tutors and simulation resources have the potential to greatly enhance the learning experience, but only when applied appropriately. Therefore, educators need to be trained adequately to use these tools effectively, and they need to be willing to receive that training [6][10].Opportunities for educators include utilizing AI-enhanced teaching tools, including virtual simulations and real-time feedback systems, which provide more personalized and adaptive learning experiences for students [14][19]. AI can also streamline assessment and grading processes, allowing educators to devote more time to developing students' higher-order thinking skills [5]. By reducing administrative burdens, educators can focus on facilitating critical analysis, problem-solving abilities, and clinical reasoning.Nevertheless, challenges persist. Training educators in AI usage is essential to ensure they can leverage these tools effectively and confidently integrate them into their teaching practices [5][8].Additionally, ethical concerns regarding the use of AI in education, such as issues of academic integrity and potential biases in AI algorithms, need to be carefully managed [18]. Educators must be equipped not only with technical skills but also with an understanding of the ethical implications of AI to guide students...