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<p>Generative artificial intelligence (GenAI) has emerged as a transformative force in medical education, offering new opportunities to enhance teaching, learning, and curriculum design. Large language models (LLMs), including systems such as ChatGPT, Gemini, and Claude, demonstrate advanced capabilities in natural language processing, clinical reasoning support, and automated content generation. This review examines the evolving role of GenAI as a catalyst for next-generation medical education, focusing on technological innovations, ethical considerations, and implementation strategies. The literature indicates that AI-driven virtual patients and simulation platforms can provide interactive clinical training environments that support history-taking, diagnostic reasoning, and decision-making skills. Additionally, AI-enabled adaptive learning systems facilitate personalized educational content, automated feedback, and real-time academic assistance, improving learner engagement and satisfaction. Despite these benefits, significant challenges remain, including concerns related to data privacy, algorithmic bias, transparency, academic integrity, and potential cognitive dependence on AI systems. Institutional preparedness for AI integration also remains limited, with many medical schools lacking formal policies, governance frameworks, and structured training programs. Addressing these gaps requires systematic curriculum reform, faculty development initiatives, and the establishment of ethical guidelines for responsible AI use. Collaborative and phased implementation strategies may help institutions integrate AI tools while maintaining educational quality and equity. Overall, generative AI holds substantial potential to augment medical training through scalable simulations, personalized learning, and enhanced communication training. However, its successful adoption depends on careful governance, ethical safeguards, and continued evaluation to ensure that AI complements rather than replaces critical clinical reasoning and professional judgment.</p>

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Generative AI as a catalyst for next-generation medical education: innovation, ethics, and implementation

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Sadaf Khan,
  • Ghizal Fatima

摘要

Generative artificial intelligence (GenAI) has emerged as a transformative force in medical education, offering new opportunities to enhance teaching, learning, and curriculum design. Large language models (LLMs), including systems such as ChatGPT, Gemini, and Claude, demonstrate advanced capabilities in natural language processing, clinical reasoning support, and automated content generation. This review examines the evolving role of GenAI as a catalyst for next-generation medical education, focusing on technological innovations, ethical considerations, and implementation strategies. The literature indicates that AI-driven virtual patients and simulation platforms can provide interactive clinical training environments that support history-taking, diagnostic reasoning, and decision-making skills. Additionally, AI-enabled adaptive learning systems facilitate personalized educational content, automated feedback, and real-time academic assistance, improving learner engagement and satisfaction. Despite these benefits, significant challenges remain, including concerns related to data privacy, algorithmic bias, transparency, academic integrity, and potential cognitive dependence on AI systems. Institutional preparedness for AI integration also remains limited, with many medical schools lacking formal policies, governance frameworks, and structured training programs. Addressing these gaps requires systematic curriculum reform, faculty development initiatives, and the establishment of ethical guidelines for responsible AI use. Collaborative and phased implementation strategies may help institutions integrate AI tools while maintaining educational quality and equity. Overall, generative AI holds substantial potential to augment medical training through scalable simulations, personalized learning, and enhanced communication training. However, its successful adoption depends on careful governance, ethical safeguards, and continued evaluation to ensure that AI complements rather than replaces critical clinical reasoning and professional judgment.