{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:58:27Z","timestamp":1775231907234,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032054784","type":"print"},{"value":"9783032054791","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:00:00Z","timestamp":1759017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:00:00Z","timestamp":1759017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05479-1_2","type":"book-chapter","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T05:16:20Z","timestamp":1758950180000},"page":"11-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ECG Report Generation with\u00a0Diagnostic Knowledge Enhanced Prompt Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9263-1775","authenticated-orcid":false,"given":"Panpan","family":"Fan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-1345","authenticated-orcid":false,"given":"Xiaodong","family":"Yue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3645-9046","authenticated-orcid":false,"given":"Yufei","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2859-6233","authenticated-orcid":false,"given":"Zhipeng","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4369-8400","authenticated-orcid":false,"given":"Jie","family":"Shi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8607-8209","authenticated-orcid":false,"given":"Zhikang","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,28]]},"reference":[{"key":"2_CR1","unstructured":"Gow, B., et\u00a0al.: Mimic-iv-ecg: diagnostic electrocardiogram matched subset. Type: dataset 6, 13\u201314 (2023)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhang, X., Zhang, S.: Kiut: knowledge-injected u-transformer for radiology report generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19809\u201319818 (2023)","DOI":"10.1109\/CVPR52729.2023.01897"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Jin, H., Che, H., Lin, Y., Chen, H.: Promptmrg: diagnosis-driven prompts for medical report generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 2607\u20132615 (2024)","DOI":"10.1609\/aaai.v38i3.28038"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Jin, Q., et al.: Medcpt: contrastive pre-trained transformers with large-scale pubmed search logs for zero-shot biomedical information retrieval. Bioinformatics 39(11), btad651 (2023)","DOI":"10.1093\/bioinformatics\/btad651"},{"issue":"3","key":"2_CR5","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1038\/s41591-025-03516-x","volume":"31","author":"L Johnson","year":"2025","unstructured":"Johnson, L., et al.: Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography. Nat. Med. 31(3), 925\u2013931 (2025)","journal-title":"Nat. Med."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"1590170","DOI":"10.3389\/fphys.2025.1590170","volume":"16","author":"K Kraik","year":"2025","unstructured":"Kraik, K., et al.: The most common errors in automatic ecg interpretation. Front. Physiol. 16, 1590170 (2025)","journal-title":"Front. Physiol."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Lee, E., et\u00a0al.: Artificial intelligence-enabled ecg for left ventricular diastolic function and filling pressure. NPJ Digit. Med. 7(1), 4 (2024)","DOI":"10.1038\/s41746-023-00993-7"},{"key":"2_CR8","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: International Conference on Machine Learning, pp. 19730\u201319742. PMLR (2023)"},{"key":"2_CR9","unstructured":"Li, J., Li, D., Xiong, C., Hoi, S.: Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888\u201312900. PMLR (2022)"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Li, M., Lin, B., Chen, Z., Lin, H., Liang, X., Chang, X.: Dynamic graph enhanced contrastive learning for chest x-ray report generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3334\u20133343 (2023)","DOI":"10.1109\/CVPR52729.2023.00325"},{"key":"2_CR11","unstructured":"Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Liu, C., Ma, Y., Kothur, K., Nikpour, A., Kavehei, O.: Biosignal copilot: leveraging the power of llms in drafting reports for biomedical signals. medrxiv (2023)","DOI":"10.1101\/2023.06.28.23291916"},{"key":"2_CR13","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: 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 (2002)","DOI":"10.3115\/1073083.1073135"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: Transfer knowledge from natural language to electrocardiography: can we detect cardiovascular disease through language models? arXiv preprint arXiv:2301.09017 (2023)","DOI":"10.18653\/v1\/2023.findings-eacl.33"},{"key":"2_CR16","unstructured":"Qiu, J., et al.: Automated cardiovascular record retrieval by multimodal learning between electrocardiogram and clinical report. In: Machine Learning for Health (ML4H), pp. 480\u2013497. PMLR (2023)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Tang, J., Xia, T., Lu, Y., Mascolo, C., Saeed, A.: Electrocardiogram report generation and question answering via retrieval-augmented self-supervised modeling. arXiv preprint arXiv:2409.08788 (2024)","DOI":"10.1109\/ICASSP49660.2025.10888594"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Vedantam, R., Lawrence\u00a0Zitnick, C., Parikh, D.: Cider: consensus-based image description evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4566\u20134575 (2015)","DOI":"10.1109\/CVPR.2015.7299087"},{"issue":"1","key":"2_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-020-0495-6","volume":"7","author":"P Wagner","year":"2020","unstructured":"Wagner, P., et al.: Ptb-xl, a large publicly available electrocardiography dataset. Scientific Data 7(1), 1\u201315 (2020)","journal-title":"Scientific Data"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Yan, B., Pei, M.: Clinical-bert: vision-language pre-training for radiograph diagnosis and reports generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 2982\u20132990 (2022)","DOI":"10.1609\/aaai.v36i3.20204"},{"key":"2_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102798","volume":"86","author":"S Yang","year":"2023","unstructured":"Yang, S., Wu, X., Ge, S., Zheng, Z., Zhou, S.K., Xiao, L.: Radiology report generation with a learned knowledge base and multi-modal alignment. Med. Image Anal. 86, 102798 (2023)","journal-title":"Med. Image Anal."},{"key":"2_CR22","unstructured":"Yu, H., Guo, P., Sano, A.: Zero-shot ecg diagnosis with large language models and retrieval-augmented generation. In: Machine Learning for Health (ML4H), pp. 650\u2013663. PMLR (2023)"},{"key":"2_CR23","first-page":"46595","volume":"36","author":"L Zheng","year":"2023","unstructured":"Zheng, L., et al.: Judging llm-as-a-judge with mt-bench and chatbot arena. Adv. Neural. Inf. Process. Syst. 36, 46595\u201346623 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR24","unstructured":"Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: Minigpt-4: enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 (2023)"}],"container-title":["Lecture Notes in Computer Science","Clinical Image-Based Procedures"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05479-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:09:07Z","timestamp":1775228947000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05479-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,28]]},"ISBN":["9783032054784","9783032054791"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05479-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,28]]},"assertion":[{"value":"28 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"CLIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Clinical Image-Based Procedures","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Democratic People's Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"clip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-clip.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}