在生成人工智能时代对眼科教育的审查
Anna Heinke1, Niloofar Radgoudarzi2, Bonnie B Huang3
1Division of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Jacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA 92037, USA.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
|August 12, 2024
概括
生成型人工智能和大型语言模型 (LLM) 在眼科教育和患者护理方面表现有前途. 解决像偏见和幻觉这样的AI挑战对于安全有效地融入临床实践至关重要.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医学教育 医学教育
背景情况:
- 生成型人工智能,特别是大型语言模型 (LLM),正在越来越多地探索其在医疗保健中的潜力.
- 眼科教育和实践可以从人工智能驱动的进步中受益.
研究的目的:
- 探索生成性AI (LLM) 在眼科教育和实践中的整合.
- 解决人工智能在眼科中的应用,益处,挑战和未来方向.
主要方法:
- 一个全面的文献审查和分析当前的AI应用程序.
- 在眼科教育课程中检查AI,包括课程和培训.
- 对AI在医学检查和临床案例研究中的表现的评估.
主要成果:
- 生成型人工智能 (LLM) 显示出改善眼科诊断准确性和患者护理的潜力.
- 人工智能应用涵盖患者,医生和医学学生教育,但面临诸如幻觉,偏见和数据限制等挑战.
- 眼科董事会考试的LLM准确性各不相同,强调需要可靠的整合和AI/数据科学培训.
结论:
- 生成性AI和LLM为眼科教育和实践提供了重大机会.
- 应对人工智能挑战需要全面的课程,道德准则和公正的,最新的培训数据.
- 未来的方向包括临床评估指标,人 - 人工智能混合模型和强大的数据隐私政策.
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