{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:12:36Z","timestamp":1780384356385,"version":"3.54.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032046239","type":"print"},{"value":"9783032046246","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"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\/978-3-032-04624-6_36","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:33:56Z","timestamp":1758000836000},"page":"613-628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and\u00a0Differential Diagnosis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1962-3916","authenticated-orcid":false,"given":"Lei","family":"Kang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-2263","authenticated-orcid":false,"given":"Xuanshuo","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3333-8812","authenticated-orcid":false,"given":"Oriol Ramos","family":"Terrades","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0414-7096","authenticated-orcid":false,"given":"Javier","family":"Vazquez-Corral","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-9697","authenticated-orcid":false,"given":"Ernest","family":"Valveny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8762-4454","authenticated-orcid":false,"given":"Dimosthenis","family":"Karatzas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"36_CR1","unstructured":"Achiam, J., et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Aghajanyan, A., Zettlemoyer, L., Gupta, S.: Intrinsic dimensionality explains the effectiveness of language model fine-tuning. arXiv preprint arXiv:2012.13255 (2020)","DOI":"10.18653\/v1\/2021.acl-long.568"},{"key":"36_CR3","unstructured":"Alam, M.M., Raff, E., Oates, T., Matuszek, C.: DDxT: deep generative transformer models for differential diagnosis. In: Deep Generative Models for Health Workshop NeurIPS 2023 (2023)"},{"issue":"240","key":"36_CR4","first-page":"1","volume":"24","author":"A Chowdhery","year":"2023","unstructured":"Chowdhery, A., et al.: Palm: scaling language modeling with pathways. J. Mach. Learn. Res. 24(240), 1\u2013113 (2023)","journal-title":"J. Mach. Learn. Res."},{"issue":"70","key":"36_CR5","first-page":"1","volume":"25","author":"HW Chung","year":"2024","unstructured":"Chung, H.W., et al.: Scaling instruction-finetuned language models. J. Mach. Learn. Res. 25(70), 1\u201353 (2024)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR6","unstructured":"Dubey, A., et al.: The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"},{"key":"36_CR7","first-page":"31306","volume":"35","author":"A Fansi Tchango","year":"2022","unstructured":"Fansi Tchango, A., Goel, R., Wen, Z., Martel, J., Ghosn, J.: Ddxplus: a new dataset for automatic medical diagnosis. Adv. Neural. Inf. Process. Syst. 35, 31306\u201331318 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"36_CR8","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1007\/s10439-023-03329-4","volume":"52","author":"J Ferdush","year":"2024","unstructured":"Ferdush, J., Begum, M., Hossain, S.T.: Chatgpt and clinical decision support: scope, application, and limitations. Ann. Biomed. Eng. 52(5), 1119\u20131124 (2024)","journal-title":"Ann. Biomed. Eng."},{"issue":"3","key":"36_CR9","doi-asserted-by":"publisher","first-page":"183","DOI":"10.34172\/hpp.2023.22","volume":"13","author":"RK Garg","year":"2023","unstructured":"Garg, R.K., Urs, V.L., Agarwal, A.A., Chaudhary, S.K., Paliwal, V., Kar, S.K.: Exploring the role of chatgpt in patient care (diagnosis and treatment) and medical research: A systematic review. Health Promot. Perspect. 13(3), 183 (2023)","journal-title":"Health Promot. Perspect."},{"key":"36_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106649","volume":"155","author":"E Hossain","year":"2023","unstructured":"Hossain, E., et al.: Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review. Comput. Biol. Med. 155, 106649 (2023)","journal-title":"Comput. Biol. Med."},{"key":"36_CR11","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Huang, S.C., Shen, L., Lungren, M.P., Yeung, S.: 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 (2021)","DOI":"10.1109\/ICCV48922.2021.00391"},{"issue":"14","key":"36_CR13","doi-asserted-by":"publisher","first-page":"6421","DOI":"10.3390\/app11146421","volume":"11","author":"D Jin","year":"2021","unstructured":"Jin, D., Pan, E., Oufattole, N., Weng, W.H., Fang, H., Szolovits, P.: What disease does this patient have? A large-scale open domain question answering dataset from medical exams. Appl. Sci. 11(14), 6421 (2021)","journal-title":"Appl. Sci."},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Jin, Q., Dhingra, B., Liu, Z., Cohen, W.W., Lu, X.: Pubmedqa: a dataset for biomedical research question answering. arXiv preprint arXiv:1909.06146 (2019)","DOI":"10.18653\/v1\/D19-1259"},{"issue":"1","key":"36_CR15","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TVCG.2018.2865027","volume":"25","author":"BC Kwon","year":"2018","unstructured":"Kwon, B.C., et al.: Retainvis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans. Visual Comput. Graphics 25(1), 299\u2013309 (2018)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Le, T.D., Nguyen, T.T., Ha, V.N.: The impact of LoRA adapters for LLMs on clinical NLP classification under data limitations. arXiv preprint arXiv:2407.19299 (2024)","DOI":"10.1109\/ACCESS.2025.3582037"},{"issue":"4","key":"36_CR17","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: When MOE meets LLMs: parameter efficient fine-tuning for multi-task medical applications. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1104\u20131114 (2024)","DOI":"10.1145\/3626772.3657722"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Luo, H., Li, S.W., Glass, J.: Knowledge grounded conversational symptom detection with graph memory networks. arXiv preprint arXiv:2101.09773 (2021)","DOI":"10.18653\/v1\/2020.clinicalnlp-1.16"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Luo, R., et al.: Biogpt: generative pre-trained transformer for biomedical text generation and mining. Briefings Bioinform. 23(6), bbac409 (2022)","DOI":"10.1093\/bib\/bbac409"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"McPeak, G., et al.: An LLM\u2019s medical testing recommendations in a Nigerian clinic: potential and limits of prompt engineering for clinical decision support. In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp. 586\u2013591. IEEE (2024)","DOI":"10.1109\/ICHI61247.2024.00094"},{"key":"36_CR22","unstructured":"Pal, A., Umapathi, L.K., Sankarasubbu, M.: Medmcqa: a large-scale multi-subject multi-choice dataset for medical domain question answering. In: Conference on Health, Inference, and Learning, pp. 248\u2013260. PMLR (2022)"},{"issue":"1","key":"36_CR23","first-page":"18","volume":"3","author":"KJ Prabhod","year":"2023","unstructured":"Prabhod, K.J.: Integrating large language models for enhanced clinical decision support systems in modern healthcare. J. Mach. Learn. Healthc. Decis. Support 3(1), 18\u201362 (2023)","journal-title":"J. Mach. Learn. Healthc. Decis. Support"},{"key":"36_CR24","unstructured":"Schmiedmayer, P., et al.: LLM on FHIR\u2013demystifying health records. arXiv preprint arXiv:2402.01711 (2024)"},{"issue":"7972","key":"36_CR25","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal, K., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172\u2013180 (2023)","journal-title":"Nature"},{"key":"36_CR26","unstructured":"Singhal, K., et al.: Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 (2023)"},{"key":"36_CR27","unstructured":"Touvron, H., et al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"issue":"1","key":"36_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13000-024-01464-7","volume":"19","author":"E Ullah","year":"2024","unstructured":"Ullah, E., Parwani, A., Baig, M.M., Singh, R.: Challenges and barriers of using large language models (LLM) such as chatgpt for diagnostic medicine with a focus on digital pathology-a recent scoping review. Diagn. Pathol. 19(1), 43 (2024)","journal-title":"Diagn. Pathol."},{"key":"36_CR29","unstructured":"Wang, H., Zhao, S., Qiang, Z., Xi, N., Qin, B., Liu, T.: Beyond direct diagnosis: LLM-based multi-specialist agent consultation for automatic diagnosis. arXiv preprint arXiv:2401.16107 (2024)"},{"issue":"1","key":"36_CR30","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1038\/s44172-024-00271-8","volume":"3","author":"S Wang","year":"2024","unstructured":"Wang, S., Zhao, Z., Ouyang, X., Liu, T., Wang, Q., Shen, D.: Interactive computer-aided diagnosis on medical image using large language models. Commun. Eng. 3(1), 133 (2024)","journal-title":"Commun. Eng."},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Wu, C., Lin, W., Zhang, X., Zhang, Y., Xie, W., Wang, Y.: PMC-LLaMA: toward building open-source language models for medicine. J. Am. Med. Inform. Assoc. ocae045 (2024)","DOI":"10.1093\/jamia\/ocae045"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Xie, Q., et al.: Me llama: foundation large language models for medical applications. arXiv preprint arXiv:2402.12749 (2024)","DOI":"10.21203\/rs.3.rs-4240043\/v1"},{"key":"36_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102748","volume":"148","author":"H Yuan","year":"2024","unstructured":"Yuan, H., Yu, S.: Efficient symptom inquiring and diagnosis via adaptive alignment of reinforcement learning and classification. Artif. Intell. Med. 148, 102748 (2024)","journal-title":"Artif. Intell. Med."},{"key":"36_CR34","unstructured":"Yuan, J., Tang, R., Jiang, X., Hu, X.: LLM for patient-trial matching: privacy-aware data augmentation towards better performance and generalizability. In: American Medical Informatics Association (AMIA) Annual Symposium (2023)"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04624-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:34:10Z","timestamp":1758000850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04624-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"ISBN":["9783032046239","9783032046246"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04624-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,17]]},"assertion":[{"value":"17 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icdar2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}