Objectives: Medical research faces substantial challenges from noisy labels
attributed to factors like inter-expert variability and machine-extracted
labels. Despite this, the adoption of label noise management remains limited,
and label noise is largely ignored. To this end, there is a critical need to
conduct a scoping review focusing on the problem space. This scoping review
aims to comprehensively review label noise management in deep learning-based
medical prediction problems, which includes label noise detection, label noise
handling, and evaluation. Research involving label uncertainty is also
included.
Methods: Our scoping review follows the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4
databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar.
Our search terms include "noisy label AND medical / healthcare / clinical",
"un-certainty AND medical / healthcare / clinical", and "noise AND medical /
healthcare / clinical".
Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A
series of practical questions in medical research are investigated. These
include the sources of label noise, the impact of label noise, the detection of
label noise, label noise handling techniques, and their evaluation.
Categorization of both label noise detection methods and handling techniques
are provided.
Discussion: From a methodological perspective, we observe that the medical
community has been up to date with the broader deep-learning community, given
that most techniques have been evaluated on medical data. We recommend
considering label noise as a standard element in medical research, even if it
is not dedicated to handling noisy labels. Initial experiments can start with
easy-to-implement methods, such as noise-robust loss functions, weighting, and
curriculum learning.

通过对深度学习的医学预测问题中的标签噪声管理进行全面审查，包括标签噪声检测、标签噪声处理和评估，本文提供了一系列实用问题的研究结果，其中包括标签噪声的来源、影响、检测、处理技术以及它们的评估，还提供了标签噪声检测方法和处理技术的分类。

医学预测问题中的有噪标签深度学习：一项范围审查

Deep learning with noisy labels in medical prediction problems: a  scoping review

The advent of generative artificial intelligence and the widespread adoption
of it in society engendered intensive debates about its ethical implications
and risks. These risks often differ from those associated with traditional
discriminative machine learning. To synthesize the recent discourse and map its
normative concepts, we conducted a scoping review on the ethics of generative
artificial intelligence, including especially large language models and
text-to-image models. Our analysis provides a taxonomy of 378 normative issues
in 19 topic areas and ranks them according to their prevalence in the
literature. The study offers a comprehensive overview for scholars,
practitioners, or policymakers, condensing the ethical debates surrounding
fairness, safety, harmful content, hallucinations, privacy, interaction risks,
security, alignment, societal impacts, and others. We discuss the results,
evaluate imbalances in the literature, and explore unsubstantiated risk
scenarios.

通过分析 378 个伦理问题的 19 个主题领域，本研究综述了生成人工智能伦理问题的分类和排名，主要关注大型语言模型和图像生成模型，为学者、从业人员和政策制定者提供了关于公平性、安全性、有害内容、幻觉、隐私、交互风险、安全性、社会影响等伦理争论的全面概述，并讨论了结果、评估了文献中的不平衡现象，并探讨了未经证实的风险场景。