Cecchini Matthew J, Borowitz Michael J, Glassy Eric F, Gullapalli Rama R, Hart Steven N, Hassell Lewis A, Homer Robert J, Jackups Ronald, McNeal Jeffrey L, Anderson Scott R
From the Department of Pathology and Laboratory Medicine, Western University and London Health Sciences Centre, London, Ontario, Canada (Cecchini).
the Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland (Borowitz).
Arch Pathol Lab Med. 2025 Feb 1;149(2):142-151. doi: 10.5858/arpa.2024-0187-RA.
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.
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。
探讨生成式AI，特别是大语言模型和多模态工具在加强病理学教育方面的应用。我们描述了它们在创造个性化学习体验、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支持学习者和教育工作者方面的潜力。
我们借鉴了关于AI在教育领域的现有文献中的见解以及该快速发展领域内共同作者的集体专业知识。案例研究突出了大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AI为丰富病理学教育提供了一个强大的工具包，为更高的参与度、可及性和个性化提供了机会。仔细考虑伦理影响、潜在风险和适当的缓解策略对于负责任和有效地整合这些技术至关重要。未来的成功在于促进AI专家和医学教育工作者之间的合作发展，优先考虑持续的人为监督和透明度，以确保生成式AI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。
