神经影像数据仓库与AI驱动医疗：神经疾病机器学习模型的全球愿望与伦理考量（表1）

数据集获取（Figshare）

• 源站标题：Table_1_Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease.DOCX
• DOI：10.3389/frai.2023.1286266.s001
• 发布日期：2024-02-19
• 文件数：1 个
• 文件总体积：686.21 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Neuroimaging_data_repositories_and_AI-driven_healthcare_Global_aspirations_vs_ethical_considerations_in_machine_learning_models_of_neurological_disease_DOCX/25242331

命令行下载

curl -L -o "Table_1_Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurologic.DOCX" "https://ndownloader.figshare.com/files/44594773"

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• Table_1_Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurologic.DOCX（686.21 KB）

文件总体积 686.21 KB（源站 API 实测）。

数据说明

Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias—ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objecti

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2023.1286266.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Neuroimaging_data_repositories_and_AI-driven_healthcare_Global_aspirations_vs_ethical_considerations_in_machine_learning_models_of_neurological_disease_DOCX/25242331）。