从数据到人工智能:评估胃肠内镜数据集的准备性
Sami Elamin1, Shreya Johri1, Pranav Rajpurkar1
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Journal of the Canadian Association of Gastroenterology
|February 24, 2025
概括
胃肠道内镜中的人工智能 (AI) 是有前途的,人工智能驱动的多重体检测在日常使用中. 然而,人工智能模型的通用性和用于更广泛的地理标志应用的多样化数据集的可用性仍然存在挑战.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 正在快速推进胃肠道学 (GI) 内镜,有众多的临床试验和已批准的算法用于多检测.
- 胃肠道领域在人工智能研究量方面领先,但与其他医疗学科相比,在FDA批准的人工智能模型的广度方面落后.
研究的目的:
- 审查GI内镜中AI的现状,重点关注数据集,挑战和未来方向.
- 突出需要高质量,多样化的数据集,以开发强大的GI内镜AI模型.
主要方法:
- 发表的随机对照试验和正在进行的GI内镜AI临床试验的综述.
- 食品和药物管理局 (FDA) 批准的AI模型和现有的GI内镜图像数据库的分析.
- 讨论人工智能进步的障碍,包括数据集大小,多样性,注释质量和道德考虑.
主要成果:
- 用于结直肠多检测的AI算法已获得批准,并且在临床使用中,人工智能驱动的诊断和囊内镜自动化是短期的机会.
- 现有的肠道内镜数据集严重偏向结肠多,缺乏患者人口统计,设备和疾病状态的多样性.
- 人工智能模型在现实世界临床实践中的通用性和稳定性仍然不一致.
结论:
- 高质量,多样化的数据集对于推进GI内镜中的AI至关重要.
- 解决数据集的局限性,注释质量和伦理问题对于克服AI实施障碍至关重要.
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