{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T05:11:00Z","timestamp":1777698660702,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819563036","type":"print"},{"value":"9789819563043","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-6304-3_15","type":"book-chapter","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:26:09Z","timestamp":1777454769000},"page":"167-178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Design and Implementation of an Intelligent Medical Record Review Assistant Based on Large Language Models"],"prefix":"10.1007","author":[{"given":"Xuguang","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengtao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunxiao","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jin, C, Yang, S, et al.: The journey of language models in understanding natural language. Web Information Systems and Applications. In:\u00a0WISA\u00a0(2024)","DOI":"10.1007\/978-981-97-7707-5_29"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Sonoda, Y., Kurokawa, R., Nakamura, Y., et al.: Diagnostic performances of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro in \u201cDiagnosis Please\u201d cases. Jpn. J.\u00a0Radiol.\u00a042, 1231\u20131235 (2024)","DOI":"10.1007\/s11604-024-01619-y"},{"key":"15_CR3","doi-asserted-by":"publisher","DOI":"10.2196\/59258","volume":"12","author":"SH Akyon","year":"2024","unstructured":"Akyon, S.H., Akyon, F.C., Camyar, A.S., et al.: Evaluating the capabilities of generative AI tools in understanding medical papers: qualitative study. JMIR Med. Inform. 12, e59258 (2024)","journal-title":"JMIR Med. Inform."},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Ntinopoulos, V., Rodriguez Cetina Biefer, H., Tudorache, I., et al.: Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation. BMJ Health Care Inform. 32(1), e101139 (2025)","DOI":"10.1136\/bmjhci-2024-101139"},{"issue":"5","key":"15_CR5","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1109\/JAS.2025.125498","volume":"12","author":"Z Deng","year":"2025","unstructured":"Deng, Z., Ma, W., Han, Q.L., et al.: Exploring DeepSeek: a survey on advances, applications, challenges and future directions. IEEE\/CAA J Autom Sinica 12(5), 872\u2013893 (2025)","journal-title":"IEEE\/CAA J Autom Sinica"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s12911-025-02954-4","volume":"25","author":"S Shool","year":"2025","unstructured":"Shool, S., Adimi, S., Saboori Amleshi, R., et al.: A systematic review of large language model (LLM) evaluations in clinical medicine. BMC Med. Inform. Decis. Mak. 25, 117 (2025)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s13755-025-00359-1","volume":"13","author":"YL Li","year":"2025","unstructured":"Li, Y.L., Wu, P.C., Zheng, A.Z., et al.: UniMRE: a unified framework for zero-shot medical relation extraction with large language models. Health Inf Sci Syst 13, 43 (2025)","journal-title":"Health Inf Sci Syst"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, L.T., Xie, H.R., Zhong, L., et al.: Explainable federated learning scheme for secure healthcare data sharing. Health Inf Sci Syst 12, 49 (2024)","DOI":"10.1007\/s13755-024-00306-6"},{"issue":"2","key":"15_CR9","first-page":"115","volume":"6","author":"J Huang","year":"2024","unstructured":"Huang, J., Lin, F., Yang, J., et al.: Prompt engineering for large generative AI models: methods, status, and prospects. Journal of Intelligent Science and Technology 6(2), 115\u2013133 (2024). [in Chinese]","journal-title":"Journal of Intelligent Science and Technology"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"S N, B P, P R, et al.: A comprehensive study on prompt engineering. Inter. J. Adv. Res. Sci. Commun. Technol., 420\u2013425 (2025)","DOI":"10.48175\/IJARSCT-23769"},{"issue":"15","key":"15_CR11","doi-asserted-by":"publisher","first-page":"2961","DOI":"10.3390\/electronics13152961","volume":"13","author":"R Patil","year":"2024","unstructured":"Patil, R., Heston, T.F., Bhuse, V.: Prompt engineering in healthcare. Electronics 13(15), 2961 (2024)","journal-title":"Electronics"},{"issue":"9","key":"15_CR12","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1093\/jamia\/ocad259","volume":"31","author":"Y Hu","year":"2024","unstructured":"Hu, Y., Chen, Q., Du, J., et al.: Improving large language models for clinical named entity recognition via prompt engineering. J. Am. Med. Inform. Assoc. 31(9), 1812\u20131820 (2024)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Kwon, T., Ong, K.T., Kang, D., et al.: Large language models are clinical reasoners: reasoning-aware diagnosis framework with prompt-generated rationales. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38(16), pp. 18417\u201318425 (2024)","DOI":"10.1609\/aaai.v38i16.29802"},{"key":"15_CR14","unstructured":"Wu, C.K., Chen, W.L., Chen, H.H.: Large language models perform diagnostic reasoning[J\/OL]. (2023). arXiv:2307.08922"},{"key":"15_CR15","unstructured":"Sahoo, P., Singh, A.K., Saha,\u00a0S., et al.: A systematic survey of prompt engineering in large language models: techniques and applications[J\/OL]. arXiv preprint arXiv:2402.07927, (2024)"},{"issue":"9","key":"15_CR16","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Fu, J., et al.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 195 (2023)","journal-title":"ACM Comput. Surv."},{"key":"15_CR17","unstructured":"Zhang, Y., Du, L., Cao, D., et al.: An examination on the effectiveness of divide-and-conquer prompting in large language models[J\/OL] (2024). arXiv:2402.05359"},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Naderi, N., Atf, Z., Lewis, P., et al.: Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs[J\/OL] (2025). https:\/\/doi.org\/10.48550\/arXiv.2506.00072","DOI":"10.48550\/arXiv.2506.00072"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, X., Schuurmans, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural Inform. Process. Syst. 35, 24824\u201324837 (2022)","DOI":"10.52202\/068431-1800"},{"key":"15_CR20","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s13755-024-00295-6","volume":"12","author":"M Sheng","year":"2024","unstructured":"Sheng, M., Wang, S.L., Zhang, Y., et al.: A multi-source heterogeneous medical data enhancement framework based on lakehouse. Health Inf Sci Syst 12, 37 (2024)","journal-title":"Health Inf Sci Syst"},{"key":"15_CR21","unstructured":"CCKS 2019. CCKS 2019 Evaluation Task: Chinese Electronic Medical Records [EB\/OL] (2019).\u00a0\u00a0https:\/\/github.com\/Toyhom\/Chinese-medical-dialogue-data"}],"container-title":["Lecture Notes in Computer Science","Health Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6304-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:26:20Z","timestamp":1777454780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6304-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819563036","9789819563043"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6304-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Health Information Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bandung","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"2 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 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":"his22025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/his-conferences.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}