眼科医学的生成人工智能:对当前的应用,机遇和挑战进行范围审查
Aljeena Rahat Qureshi1, Jonathan A Micieli2,3,4, Jovi C Y Wong5
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Eye (London, England)
|September 27, 2025
概括
生成型人工智能 (AI) 在眼科中为诊断和成像提供了前景. 本综述绘制了AI模型,应用和挑战,强调了临床整合需要验证和监管明确性的需要.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 生成型人工智能 (AI) 在眼科中显示出提高诊断准确度,增强数据集和模拟疾病进展的潜力.
- 目前文献中缺乏对生成性AI模型及其在眼科中的应用进行全面的映射.
研究的目的:
- 识别和分类用于眼科的生成性AI模型.
- 检查这些AI模型的临床和研究应用.
- 评估在临床实践中部署生成AI的可行性,挑战和未来研究方向.
主要方法:
- 在过去10年中,主要研究文章发表在英语中的范围审查,重点是眼科成像的生成AI方法.
- 在MEDLINE,Embase和Web of Science进行了搜索,截至2024年12月24日.
- 数据提取包括研究设计,AI类型和临床结果,其中一个审查员选标题,摘要和全文.
主要成果:
- 包括40项研究,主要应用在数据增强 (11项研究) 和预测建模 (11项研究),其次是图像增强 (8项),细分 (7项) 和教育/可解释性 (3).
- 生成对抗网络 (GAN) 主导,最近扩散模型,变异自编码器 (VAE) 和基于流的模型的兴起.
- 确定的主要实施障碍包括数据可用性,监管考虑和模型稳定性.
结论:
- 生成性人工智能越来越多地应用于眼科,像扩散模型这样的新兴趋势显示出显著的潜力.
- 虽然有希望,但强大的现实世界验证和明确的监管途径对于这些人工智能技术的成功临床整合至关重要.
- 需要进一步的研究来解决实施障碍,并确保在眼科实践中安全有效地使用生成人工智能.
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