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6/6/2025 6:59:07 AM | Browse: 9 | Download: 17
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Received |
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2025-03-16 08:18 |
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Peer-Review Started |
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2025-03-16 08:18 |
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To Make the First Decision |
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Return for Revision |
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2025-03-31 09:35 |
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Revised |
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2025-04-14 11:52 |
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Second Decision |
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2025-05-26 02:42 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-05-26 08:11 |
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Articles in Press |
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2025-05-26 08:11 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-06-03 06:15 |
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Publish the Manuscript Online |
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2025-06-06 06:23 |
| ISSN |
2644-3260 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Health Care Sciences & Services |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence assisted ultrasound report generation
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Jia-Hui Zeng, Kai-Kai Zhao and Ningbo Zhao |
| Funding Agency and Grant Number |
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| Corresponding Author |
Ningbo Zhao, Associate Chief Physician, Associate Professor, Department of Ultrasound, National Clinical Research Centre for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Department of Ultrasound, The Third People's Hospital of Shenzhen, Shenzhen, 518116, Guangdong, China. Electronic address: 971599910@qq.com., Shenzhen Guangdong Province, 美国境外, China. 971599910@qq.com |
| Key Words |
Large language model; Natural language generation; Vision-language models; Ultrasound report; Artificial intelligence |
| Core Tip |
This article investigates artificial intelligence assisted ultrasound report generation using vision-language models, addressing challenges unique to ultrasound imaging, such as numerical measurement accuracy, multi-image correlation, and template integration. Unlike standardized radiological imaging, ultrasound variability stems from operator-dependent acquisition and image noise, complicating automated analysis. The framework integrates Transformer-based Optical Character Recognition for measurement extraction, pseudo-case synthesis for data augmentation, and cross-modal alignment to improve report precision. Innovations include leveraging historical reports, video data, and clinical expertise to enhance diagnostic outputs. Ethical protocols ensure data privacy, while template-driven workflows enhance clinical relevance. Future advancements focus on real-time reporting, personalized diagnostics, and multimodal models like GPT-4 Vision. This article bridges AI capabilities with clinical demands to standardize reports, reduce workloads, and support ultrasound decision-making. |
| Publish Date |
2025-06-06 06:23 |
| Citation |
<p>Zeng JH, Zhao KK, Zhao N. Artificial intelligence assisted ultrasound report generation. <i>Artif Intell Med Imaging</i> 2025; 6(1): 107069</p> |
| URL |
https://www.wjgnet.com/2644-3260/full/v6/i1/107069.htm |
| DOI |
https://dx.doi.org/10.35711/aimi.v6.i1.107069 |
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