Divisions of Health Informatics & Emergency Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania.
Pediatrics. 2024 Sep 1;154(3). doi: 10.1542/peds.2023-064683.
Generative artificial intelligence, especially large language models (LLMs), has the potential to affect every level of pediatric education and training. Demonstrating speed and adaptability, LLMs can aid educators, trainees, and practicing pediatricians with tasks such as enhancing curriculum design through the creation of cases, videos, and assessments; creating individualized study plans and providing real-time feedback for trainees; and supporting pediatricians by enhancing information searches, clinic efficiency, and bedside teaching. LLMs can refine patient education materials to address patients' specific needs. The current versions of LLMs sometimes provide "hallucinations" or incorrect information but are likely to improve. There are ethical concerns related to bias in the output of LLMs, the potential for plagiarism, and the possibility of the overuse of an online tool at the expense of in-person learning. The potential benefits of LLMs in pediatric education can outweigh the potential risks if employed judiciously by content experts who conscientiously review the output. All stakeholders must firmly establish rules and policies to provide rigorous guidance and assure the safe and proper use of this transformative tool in the care of the child. In this article, we outline the history, current uses, and challenges with generative artificial intelligence in pediatrics education. We provide examples of LLM output, including performance on a pediatrics examination guide and the creation of patient care instructions. Future directions to establish a safe and appropriate path for the use of LLMs will be discussed.
生成式人工智能，尤其是大型语言模型（LLMs），有可能影响儿科教育和培训的各个层面。LLMs 具有速度和适应性，能够帮助教育工作者、学员和执业儿科医生完成多项任务，例如通过创建病例、视频和评估来增强课程设计；为学员创建个性化的学习计划并提供实时反馈；通过增强信息搜索、提高诊所效率和床边教学来支持儿科医生。LLMs 还可以改进患者教育材料，以满足患者的特定需求。目前的 LLM 版本有时会提供“幻觉”或错误信息，但它们很可能会得到改进。与 LLM 输出的偏见、剽窃的潜在风险以及过度使用在线工具而牺牲面对面学习的可能性相关的伦理问题。如果由认真审查输出的内容专家明智地使用，那么 LLM 在儿科教育中的潜在益处可能超过潜在风险。所有利益相关者都必须坚决制定规则和政策，为这一变革性工具在儿童护理中的安全和正确使用提供严格的指导。在本文中，我们概述了生成式人工智能在儿科教育中的历史、当前用途和挑战。我们提供了 LLM 输出的示例，包括在儿科考试指南上的表现和患者护理说明的创建。未来将讨论建立 LLM 使用的安全和适当途径的方向。
