Fairness in artificial intelligence‐driven multi‐organ image segmentation
Abstract Fairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision‐making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment p...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | iRADIOLOGY |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ird3.101 |
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| _version_ | 1850243914967023616 |
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| author | Qing Li Yizhe Zhang Longyu Sun Mengting Sun Meng Liu Zian Wang Qi Wang Shuo Wang Chengyan Wang |
| author_facet | Qing Li Yizhe Zhang Longyu Sun Mengting Sun Meng Liu Zian Wang Qi Wang Shuo Wang Chengyan Wang |
| author_sort | Qing Li |
| collection | DOAJ |
| description | Abstract Fairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision‐making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups, resulting in severe consequences for patients and society. In medical artificial intelligence (AI), the fairness of multi‐organ segmentation is imperative to augment the integration of models into clinical practice. As the use of multi‐organ segmentation in medical image analysis expands, it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity. However, comprehensive studies assessing the problem of fairness in multi‐organ segmentation remain lacking. This study aimed to provide an overview of the fairness problem in multi‐organ segmentation. We first define fairness and discuss the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi‐organ segmentation, focusing mainly on datasets and models. We then present strategies to potentially improve fairness in multi‐organ segmentation. Additionally, we highlight the challenges and limitations of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi‐organ segmentation. |
| format | Article |
| id | doaj-art-c8b86dcde08c480f980e6fa9c156cb5e |
| institution | OA Journals |
| issn | 2834-2860 2834-2879 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | iRADIOLOGY |
| spelling | doaj-art-c8b86dcde08c480f980e6fa9c156cb5e2025-08-20T01:59:52ZengWileyiRADIOLOGY2834-28602834-28792024-12-012653955610.1002/ird3.101Fairness in artificial intelligence‐driven multi‐organ image segmentationQing Li0Yizhe Zhang1Longyu Sun2Mengting Sun3Meng Liu4Zian Wang5Qi Wang6Shuo Wang7Chengyan Wang8Human Phenome Institute and Shanghai Pudong Hospital Fudan University Shanghai ChinaSchool of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu ChinaHuman Phenome Institute and Shanghai Pudong Hospital Fudan University Shanghai ChinaHuman Phenome Institute and Shanghai Pudong Hospital Fudan University Shanghai ChinaHuman Phenome Institute and Shanghai Pudong Hospital Fudan University Shanghai ChinaSchool of Computer Science Fudan University Shanghai ChinaSchool of Basic Medical Sciences Fudan University Shanghai ChinaDigital Medical Research Center Fudan University Shanghai ChinaHuman Phenome Institute and Shanghai Pudong Hospital Fudan University Shanghai ChinaAbstract Fairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision‐making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups, resulting in severe consequences for patients and society. In medical artificial intelligence (AI), the fairness of multi‐organ segmentation is imperative to augment the integration of models into clinical practice. As the use of multi‐organ segmentation in medical image analysis expands, it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity. However, comprehensive studies assessing the problem of fairness in multi‐organ segmentation remain lacking. This study aimed to provide an overview of the fairness problem in multi‐organ segmentation. We first define fairness and discuss the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi‐organ segmentation, focusing mainly on datasets and models. We then present strategies to potentially improve fairness in multi‐organ segmentation. Additionally, we highlight the challenges and limitations of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi‐organ segmentation.https://doi.org/10.1002/ird3.101artificial intelligencebiasfairnessmedical imagemulti‐organ segmentation |
| spellingShingle | Qing Li Yizhe Zhang Longyu Sun Mengting Sun Meng Liu Zian Wang Qi Wang Shuo Wang Chengyan Wang Fairness in artificial intelligence‐driven multi‐organ image segmentation iRADIOLOGY artificial intelligence bias fairness medical image multi‐organ segmentation |
| title | Fairness in artificial intelligence‐driven multi‐organ image segmentation |
| title_full | Fairness in artificial intelligence‐driven multi‐organ image segmentation |
| title_fullStr | Fairness in artificial intelligence‐driven multi‐organ image segmentation |
| title_full_unstemmed | Fairness in artificial intelligence‐driven multi‐organ image segmentation |
| title_short | Fairness in artificial intelligence‐driven multi‐organ image segmentation |
| title_sort | fairness in artificial intelligence driven multi organ image segmentation |
| topic | artificial intelligence bias fairness medical image multi‐organ segmentation |
| url | https://doi.org/10.1002/ird3.101 |
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