{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T17:40:41Z","timestamp":1793122841160,"version":"4.5.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100031882","name":"World Rugby","doi-asserted-by":"publisher","award":["2403-008-19513"],"award-info":[{"award-number":["2403-008-19513"]}],"id":[{"id":"10.13039\/100031882","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1A2B5B03001980"],"award-info":[{"award-number":["NRF-2020R1A2B5B03001980"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1016\/j.compmedimag.2025.102697","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T16:22:38Z","timestamp":1767025358000},"page":"102697","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Wholistic report generation for Breast ultrasound using LangChain"],"prefix":"10.1016","volume":"127","author":[{"given":"Jaeyoung","family":"Huh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye Shin","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun Jeong","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-9609","authenticated-orcid":false,"given":"Jong Chul","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2025.102697_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.compmedimag.2025.102697_b2","doi-asserted-by":"crossref","unstructured":"Bannur, S., Hyland, S., Liu, Q., Perez-Garcia, F., Ilse, M., Castro, D.C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al., 2023. Learning to exploit temporal structure for biomedical vision-language processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 15016\u201315027.","DOI":"10.1109\/CVPR52729.2023.01442"},{"key":"10.1016\/j.compmedimag.2025.102697_b3","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"Making the most of text semantics to improve biomedical vision\u2013language processing","author":"Boecking","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102697_b4","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2025.102697_b5","unstructured":"Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G., 2021a. Pix2seq: A Language Modeling Framework for Object Detection. In: International Conference on Learning Representations."},{"key":"10.1016\/j.compmedimag.2025.102697_b6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Shen, Y., Song, Y., Wan, X., 2021b. Cross-modal Memory Networks for Radiology Report Generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 5904\u20135914.","DOI":"10.18653\/v1\/2021.acl-long.459"},{"key":"10.1016\/j.compmedimag.2025.102697_b7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Song, Y., Chang, T.-H., Wan, X., 2020. Generating Radiology Reports via Memory-driven Transformer. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. EMNLP, pp. 1439\u20131449.","DOI":"10.18653\/v1\/2020.emnlp-main.112"},{"key":"10.1016\/j.compmedimag.2025.102697_b8","unstructured":"Denkowski, M., Lavie, A., 2011. Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems. In: Proceedings of the Sixth Workshop on Statistical Machine Translation. pp. 85\u201391."},{"key":"10.1016\/j.compmedimag.2025.102697_b9","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"1275","article-title":"Producing radiologist-quality reports for interpretable deep learning","author":"Gale","year":"2019"},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102697_b10","doi-asserted-by":"crossref","first-page":"149","DOI":"10.2174\/1573405618666220329092537","article-title":"AI-assisted method for efficiently generating breast ultrasound screening reports","volume":"19","author":"Ge","year":"2023","journal-title":"Curr. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102697_b11","doi-asserted-by":"crossref","unstructured":"Huang, S.-C., Shen, L., Lungren, M.P., Yeung, S., 2021. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3942\u20133951.","DOI":"10.1109\/ICCV48922.2021.00391"},{"key":"10.1016\/j.compmedimag.2025.102697_b12","series-title":"On the automatic generation of medical imaging reports","author":"Jing","year":"2017"},{"key":"10.1016\/j.compmedimag.2025.102697_b13","doi-asserted-by":"crossref","unstructured":"Jing, B., Xie, P., Xing, E., 2018. On the Automatic Generation of Medical Imaging Reports. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2577\u20132586.","DOI":"10.18653\/v1\/P18-1240"},{"key":"10.1016\/j.compmedimag.2025.102697_b14","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al., 2023. Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.compmedimag.2025.102697_b15","series-title":"Unified chest X-ray and radiology report generation model with multi-view chest X-rays","author":"Lee","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b16","article-title":"Hybrid retrieval-generation reinforced agent for medical image report generation","volume":"31","author":"Li","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2025.102697_b17","doi-asserted-by":"crossref","unstructured":"Li, C.Y., Liang, X., Hu, Z., Xing, E.P., 2019. Knowledge-driven encode, retrieve, paraphrase for medical image report generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33. pp. 6666\u20136673.","DOI":"10.1609\/aaai.v33i01.33016666"},{"key":"10.1016\/j.compmedimag.2025.102697_b18","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"13094","article-title":"Trocr: Transformer-based optical character recognition with pre-trained models","author":"Li","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b19","series-title":"Ultrasound report generation with cross-modality feature alignment via unsupervised guidance","author":"Li","year":"2024"},{"key":"10.1016\/j.compmedimag.2025.102697_b20","unstructured":"Lin, C.-Y., 2004. Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out. pp. 74\u201381."},{"key":"10.1016\/j.compmedimag.2025.102697_b21","doi-asserted-by":"crossref","unstructured":"Liu, F., Wu, X., Ge, S., Fan, W., Zou, Y., 2021. Exploring and distilling posterior and prior knowledge for radiology report generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13753\u201313762.","DOI":"10.1109\/CVPR46437.2021.01354"},{"issue":"12","key":"10.1016\/j.compmedimag.2025.102697_b22","doi-asserted-by":"crossref","first-page":"6070","DOI":"10.1109\/JBHI.2022.3207502","article-title":"Multi-modal understanding and generation for medical images and text via vision-language pre-training","volume":"26","author":"Moon","year":"2022","journal-title":"IEEE J. Biomed. Health Informatics"},{"issue":"7956","key":"10.1016\/j.compmedimag.2025.102697_b23","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.compmedimag.2025.102697_b24","series-title":"Med-flamingo: a multimodal medical few-shot learner","author":"Moor","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b25","series-title":"Capabilities of gpt-4 on medical challenge problems","author":"Nori","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b26","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2025.102697_b27","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.-J., 2002. Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. pp. 311\u2013318.","DOI":"10.3115\/1073083.1073135"},{"key":"10.1016\/j.compmedimag.2025.102697_b28","series-title":"Talm: Tool augmented language models","author":"Parisi","year":"2022"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102697_b29","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.1080\/03610918.2014.931971","article-title":"Overview of friedman\u2019s test and post-hoc analysis","volume":"44","author":"Pereira","year":"2015","journal-title":"Comm. Statist. Simulation Comput."},{"key":"10.1016\/j.compmedimag.2025.102697_b30","series-title":"Toolllm: Facilitating large language models to master 16000+ real-world apis","author":"Qin","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b31","doi-asserted-by":"crossref","unstructured":"Qin, H., Song, Y., 2022. Reinforced cross-modal alignment for radiology report generation. In: Findings of the Association for Computational Linguistics: ACL 2022. pp. 448\u2013458.","DOI":"10.18653\/v1\/2022.findings-acl.38"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102697_b32","first-page":"5485","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compmedimag.2025.102697_b33","series-title":"Toolformer: Language models can teach themselves to use tools","author":"Schick","year":"2023"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102697_b34","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1002\/pri.66","article-title":"The use and interpretation of the friedman test in the analysis of ordinal-scale data in repeated measures designs","volume":"1","author":"Sheldon","year":"1996","journal-title":"Physiother. Res. Int."},{"key":"10.1016\/j.compmedimag.2025.102697_b35","series-title":"Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface","author":"Shen","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b36","doi-asserted-by":"crossref","unstructured":"Shin, H.-C., Lu, L., Kim, L., Seff, A., Yao, J., Summers, R.M., 2015. Interleaved text\/image deep mining on a very large-scale radiology database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1090\u20131099.","DOI":"10.1109\/CVPR.2015.7298712"},{"key":"10.1016\/j.compmedimag.2025.102697_b37","doi-asserted-by":"crossref","unstructured":"Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M., 2016. Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2497\u20132506.","DOI":"10.1109\/CVPR.2016.274"},{"issue":"7972","key":"10.1016\/j.compmedimag.2025.102697_b38","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.compmedimag.2025.102697_b39","series-title":"Xraygpt: Chest radiographs summarization using medical vision-language models","author":"Thawkar","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b40","series-title":"Llama: Open and efficient foundation language models","author":"Touvron","year":"2023"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102697_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.metrad.2023.100033","article-title":"R2gengpt: Radiology report generation with frozen llms","volume":"1","author":"Wang","year":"2023","journal-title":"Meta-Radiology"},{"key":"10.1016\/j.compmedimag.2025.102697_b42","series-title":"Chatcad: Interactive computer-aided diagnosis on medical image using large language models","author":"Wang","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b43","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, L., Wang, L., Li, X., 2021. A self-boosting framework for automated radiographic report generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2433\u20132442.","DOI":"10.1109\/CVPR46437.2021.00246"},{"key":"10.1016\/j.compmedimag.2025.102697_b44","doi-asserted-by":"crossref","unstructured":"Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., Xie, S., 2023. Convnext v2: Co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16133\u201316142.","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"10.1016\/j.compmedimag.2025.102697_b45","series-title":"Reinforced medical report generation with x-linear attention and repetition penalty","author":"Xu","year":"2020"},{"key":"10.1016\/j.compmedimag.2025.102697_b46","series-title":"ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders","author":"Xu","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b47","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"2982","article-title":"Clinical-BERT: Vision-language pre-training for radiograph diagnosis and reports generation","author":"Yan","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102697_b48","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.neucom.2020.09.084","article-title":"Automatic ultrasound image report generation with adaptive multimodal attention mechanism","volume":"427","author":"Yang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compmedimag.2025.102697_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102798","article-title":"Radiology report generation with a learned knowledge base and multi-modal alignment","volume":"86","author":"Yang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2025.102697_b50","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22","first-page":"721","article-title":"Automatic radiology report generation based on multi-view image fusion and medical concept enrichment","author":"Yuan","year":"2019"},{"key":"10.1016\/j.compmedimag.2025.102697_b51","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Xie, Y., Xing, F., McGough, M., Yang, L., 2017. Mdnet: A semantically and visually interpretable medical image diagnosis network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6428\u20136436.","DOI":"10.1109\/CVPR.2017.378"},{"key":"10.1016\/j.compmedimag.2025.102697_b52","series-title":"ChatRadio-valuer: A chat large language model for generalizable radiology report generation based on multi-institution and multi-system data","author":"Zhong","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b53","series-title":"Skingpt-4: An interactive dermatology diagnostic system with visual large language model","author":"Zhou","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102697_b54","series-title":"Minigpt-4: Enhancing vision-language understanding with advanced large language models","author":"Zhu","year":"2023"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089561112500206X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089561112500206X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T01:00:45Z","timestamp":1773968445000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S089561112500206X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":54,"alternative-id":["S089561112500206X"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102697","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Wholistic report generation for Breast ultrasound using LangChain","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102697","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102697"}}