Harnessing the Power of Generative Artificial Intelligence in Pathology Education

• Published in: Archives of pathology & laboratory medicine (1976)
• Main Authors: Cecchini, Matthew J, Borowitz, Michael J, Glassy, Eric F, Gullapalli, Rama R, Hart, Steven N, Hassell, Lewis A, Homer, Robert J, Jackups, Jr, Ronald, McNeal, Jeffrey L, Anderson, Scott R
• Format: Journal Article
• Language: English
• Published: United States 30.09.2024
• DOI: 10.5858/arpa.2024-0187-RA
• ISSN: 0003-9985; 1543-2165

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