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Kwangil Yim

As seen in: Nature, Wiley Online Library, MDPI
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Articles

Sequential question answering AI for hierarchical gastric pathology diagnosis

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By Chaeyeon Lee, Seung-Won Jung, Kwangil Yim, Yosep Chong, Jin Tae Kwak, Khang Nguyen Quoc, Jinsol Song
| Nature Verified
Abstract Artificial intelligence in digital pathology has evolved from task-specific models to multimodal foundation models. However, clinical adoption remains limited by a misalignment between model design and the hierarchical, conditional nature of diagnostic reasoning. In complex domains, such as gastric pathology, diagnosis requires navigating strictly dependent steps where downstream tasks are only clinically valid given specific upstream findings.
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Tumour budding in pretreatment cervical biopsies: a prognosticator for personalised therapy in the era of precision oncology

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By Kwangil Yim, Chan Kim, Jin-Hwi Kim, Yong Seok Lee
| Wiley Online Library Verified
Conflict of interests The authors report no relevant conflicts of interest. Data availability statement The data that support the findings of this study are available on request from the corresponding authors. Supporting Information Filename Description his70053-sup-0001-Figure S1.tifTIFF image, 12.5 MB Figure S1. Supplemental microscopic images for tumour budding (TB) assessment methods.
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Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid

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By Hyung Kyung Kim, Jeonghyo Lee, Kwangil Yim, Eunkyung Han
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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