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• W4387435284 abstract "Artificial intelligence (AI) holds tremendous promise for medical image analysis and computer-aided diagnosis, but various challenges must be addressed to enable its widespread adoption and impact in patient care. Open science, specifically through open-source code and public databases, brings multiple benefits to the progress of AI in medical imaging. It is expected to facilitate research output sharing, promote collaboration among researchers, improve the reproducibility of findings, and foster innovation. However, it is important to recognize that the current state of open-source research, particularly with respect to the large, public datasets commonly used in medical imaging AI, is inherently centered around high-income countries (HIC) and privileged populations. Low- and middle-income countries (LMIC) often face several limitations in contributing to and benefiting from open science research in this domain, such as inadequate digital infrastructure, limited funding for research and development, and a scarcity of healthcare and data science professionals. This may lead to further global disparities in health equity as AI-based clinical decision support systems continue to be implemented in practice. While transfer learning and distributed learning hold promise in addressing some challenges related to limited and non-public data in LMIC, practical obstacles arise when dealing with small, lower-quality datasets, resource constraints, and the need for tailored local implementation of these models. In this commentary, we explore the relationship between open-source models and public medical imaging data repositories in the context of transfer learning and distributed learning, specifically considering their implications for global health equity." @default.
• W4387435284 created "2023-10-09" @default.
• W4387435284 creator A5021288984 @default.
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• W4387435284 date "2023-01-01" @default.
• W4387435284 modified "2023-10-09" @default.
• W4387435284 title "On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity" @default.
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• W4387435284 doi "https://doi.org/10.1007/978-3-031-45249-9_28" @default.
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