How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
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2026
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| author | Liu, Yuanye Shi, Nannan Zhang, Zhejia Zhang, Hanxiao Wang, Boya Yu, Derong Wang, Nao Jin, Yuxin Zhou, Yang Yuan, Kunhao Wang, Siqi Yang, Lida Qiao, Xu Liu, Wentao He, Xuelei Hong, Xin Zheng, Guoyan Chen, Xin Yang, Guang-Zhong Zhang, Le Li, Lei Shi, Yuxin Zhuang, Xiahai |
| author_facet | Liu, Yuanye Shi, Nannan Zhang, Zhejia Zhang, Hanxiao Wang, Boya Yu, Derong Wang, Nao Jin, Yuxin Zhou, Yang Yuan, Kunhao Wang, Siqi Yang, Lida Qiao, Xu Liu, Wentao He, Xuelei Hong, Xin Zheng, Guoyan Chen, Xin Yang, Guang-Zhong Zhang, Le Li, Lei Shi, Yuxin Zhuang, Xiahai |
| contents | Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneous, multi-center conditions that define clinical practice. To address this gap, we introduce LiFS, a large-scale dataset and benchmark derived from the MICCAI 2025 CARE-Liver challenge, comprising 610 patients across multiple centers and scanners with multi-sequence MRI. To the best of our knowledge, LiFS is the first benchmark providing complete gadoxetic acid-enhanced sequences with histopathology-confirmed annotations from diverse real-world scanners. Through systematic evaluation of 9 independently developed methods selected from 96 registered teams against in-cohort radiologist reference results, our findings address how far current AI has progressed toward clinical-level liver fibrosis staging from three complementary perspectives. First, against radiologists, the best AI methods were broadly comparable to the senior radiologist and significantly exceeded the junior radiologist in selected settings, while median AI performance generally approached junior-radiologist levels. Second, from a data perspective, cross-center heterogeneity, label imbalance, and contrast-enhanced sequence variability emerge as the dominant challenges for AI methods. Third, from a technical perspective, methodological design choices, including spatial registration, input dimensionality, multi-modal fusion strategy, and backbone architecture, appear to modulate cross-center robustness, although no single choice alone closes the gap. Overall, LiFS provides a rigorous real-world benchmark for positioning the current state of AI in liver fibrosis staging and for enabling future research on the key challenges that limit clinically reliable deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_25595 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark Liu, Yuanye Shi, Nannan Zhang, Zhejia Zhang, Hanxiao Wang, Boya Yu, Derong Wang, Nao Jin, Yuxin Zhou, Yang Yuan, Kunhao Wang, Siqi Yang, Lida Qiao, Xu Liu, Wentao He, Xuelei Hong, Xin Zheng, Guoyan Chen, Xin Yang, Guang-Zhong Zhang, Le Li, Lei Shi, Yuxin Zhuang, Xiahai Computer Vision and Pattern Recognition Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneous, multi-center conditions that define clinical practice. To address this gap, we introduce LiFS, a large-scale dataset and benchmark derived from the MICCAI 2025 CARE-Liver challenge, comprising 610 patients across multiple centers and scanners with multi-sequence MRI. To the best of our knowledge, LiFS is the first benchmark providing complete gadoxetic acid-enhanced sequences with histopathology-confirmed annotations from diverse real-world scanners. Through systematic evaluation of 9 independently developed methods selected from 96 registered teams against in-cohort radiologist reference results, our findings address how far current AI has progressed toward clinical-level liver fibrosis staging from three complementary perspectives. First, against radiologists, the best AI methods were broadly comparable to the senior radiologist and significantly exceeded the junior radiologist in selected settings, while median AI performance generally approached junior-radiologist levels. Second, from a data perspective, cross-center heterogeneity, label imbalance, and contrast-enhanced sequence variability emerge as the dominant challenges for AI methods. Third, from a technical perspective, methodological design choices, including spatial registration, input dimensionality, multi-modal fusion strategy, and backbone architecture, appear to modulate cross-center robustness, although no single choice alone closes the gap. Overall, LiFS provides a rigorous real-world benchmark for positioning the current state of AI in liver fibrosis staging and for enabling future research on the key challenges that limit clinically reliable deployment. |
| title | How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.25595 |