癌症诊断,预后和预测中的计算病理学 - 现在和前景
Gregory Verghese1,2, Jochen K Lennerz3, Danny Ruta4
1School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
The Journal of pathology
|August 14, 2023
概括
计算病理学使用人工智能 (AI) 来分析组织图像用于诊断和生物标志物发现. 克服临床整合挑战是实现其在病理学中的全部潜力的关键.
科学领域:
- 数字病理学数字病理学
- 医学中的人工智能
- 组织病理学图像分析分析.
背景情况:
- 计算病理学利用深度学习来解释组织病理学图像.
- 人工智能的进步为自动化诊断和发现新生物标记物提供了潜力.
- 计算病理学的临床整合面临着重大的运营,技术,监管,道德,金融和文化障碍.
研究的目的:
- 从病理学家的角度研究计算病理学.
- 为了绘制当前的翻译研究格局.
- 解决阻碍临床采用和实施的挑战.
主要方法:
- 审查当前的翻译研究在计算病理学.
- 临床效用和影响的评估.
- 对临床实施的常见障碍的分析.
主要成果:
- 确定了计算病理学的潜力与其临床应用之间的差距.
- 详细介绍了阻碍广泛采用的多方面的挑战.
- 强调了病理学家在解决这些障碍方面的观点的重要性.
结论:
- 需要当代策略来加速将计算病理学纳入临床实践.
- 解决已确定的挑战对于实现AI在病理学中的好处至关重要.
- 病理学家的观点对于成功实施和未来的发展至关重要.
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