Addressing fairness in artificial intelligence for medical imaging
A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/213281 |
| Online Access: | http://hdl.handle.net/11336/213281 |
| Access Level: | Open access |
| Keyword: | Fairness Artificial Intelligence Medical Imaging https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Summary: | A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and comment on the potential sources of biases, as well as the strategies available to mitigate them. Finally, we analyze the current state of the field, identifying strengths and highlighting areas of vacancy, challenges and opportunities that lie ahead. |
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