Harnessing the Power of Generative Artificial Intelligence in Pathology Education.

Matthew J Cecchini, Michael J Borowitz, Eric F Glassy, Rama R Gullapalli, Steven N Hart, Lewis A Hassell, Robert J Homer, Ronald Jackups, Jeffrey L McNeal, Scott R Anderson
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Abstract

Context.—: Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches.

Objective.—: To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum.

Data sources.—: We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education.

Conclusions.—: Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.

在病理学教育中利用生成式人工智能的力量。
背景生成式人工智能(AI)技术正在迅速改变包括病理学在内的众多领域,并具有彻底改变教育方法的巨大潜力:探索生成式人工智能的应用,特别是大型语言模型和多模态工具,以加强病理学教育。我们描述了它们在创建个性化学习体验、简化内容开发、扩大教育资源获取途径以及在整个培训和实践过程中为学习者和教育者提供支持的潜力:我们借鉴了有关教育领域人工智能的现有文献中的见解,以及共同作者在这一快速发展的领域中的集体专业知识。案例研究突出了大型语言模型的实际应用,展示了在病理学教育中实施这些技术的潜在益处和独特挑战:生成式人工智能为丰富病理学教育提供了一个强大的工具包,为提高参与度、可及性和个性化提供了机会。认真考虑伦理影响、潜在风险和适当的缓解策略对于负责任地有效整合这些技术至关重要。未来的成功在于促进人工智能专家与医学教育工作者之间的合作开发,优先考虑持续的人工监督和透明度,以确保生成式人工智能增强而非取代教育工作者在病理学培训和实践中的重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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