{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:00:29Z","timestamp":1789984829723,"version":"4.0.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032380586","type":"print"},{"value":"9783032380593","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38059-3_18","type":"book-chapter","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:06:21Z","timestamp":1789981581000},"page":"184-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hallucination Detection and\u00a0Correction in\u00a0Medical VLMs via\u00a0Counter-Evidence Verification"],"prefix":"10.1007","author":[{"given":"Nan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linchao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"issue":"8","key":"18_CR1","doi-asserted-by":"publisher","first-page":"3846","DOI":"10.3390\/app12083846","volume":"12","author":"J An","year":"2022","unstructured":"An, J., Joe, I.: Attention map-guided visual explanations for deep neural networks. Appl. Sci. 12(8), 3846 (2022)","journal-title":"Appl. Sci."},{"key":"18_CR2","unstructured":"Bae, S., et al.: Mimic-ext-mimic-cxr-vqa: a complex, diverse, and large-scale visual question answering dataset for chest x-ray images (2024)"},{"key":"18_CR3","unstructured":"Bai, J., et al.: Qwen-vl: a frontier large vision-language model with versatile abilities, 1(2), 3 (2023). arXiv preprint arXiv:2308.12966"},{"key":"18_CR4","unstructured":"Bannur, S., et al.: Maira-2: grounded radiology report generation. arXiv preprint arXiv:2406.04449 (2024)"},{"key":"18_CR5","unstructured":"Chen, J., et al.: Detecting and evaluating medical hallucinations in large vision language models. arXiv preprint arXiv:2406.10185 (2024)"},{"issue":"2","key":"18_CR6","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/jamia\/ocv080","volume":"23","author":"D Demner-Fushman","year":"2015","unstructured":"Demner-Fushman, D., et al.: Preparing a collection of radiology examinations for distribution and retrieval. J. Am. Med. Inf. Assoc. 23(2), 304\u2013310 (2015)","journal-title":"J. Am. Med. Inf. Assoc."},{"issue":"8017","key":"18_CR7","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s41586-024-07421-0","volume":"630","author":"S Farquhar","year":"2024","unstructured":"Farquhar, S., Kossen, J., Kuhn, L., Gal, Y.: Detecting hallucinations in large language models using semantic entropy. Nature 630(8017), 625\u2013630 (2024)","journal-title":"Nature"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Gu, Z., Chen, J., Liu, F., Yin, C., Zhang, P.: Medvh: toward systematic evaluation of hallucination for large vision language models in the medical context. Adv. Intell. Syst. 2500255 (2025)","DOI":"10.1002\/aisy.202500255"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Gunjal, A., Yin, J., Bas, E.: Detecting and preventing hallucinations in large vision language models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 18135\u201318143 (2024)","DOI":"10.1609\/aaai.v38i16.29771"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6(1), 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"issue":"1","key":"18_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.251","volume":"5","author":"JJ Lau","year":"2018","unstructured":"Lau, J.J., Gayen, S., Ben Abacha, A., Demner-Fushman, D.: A dataset of clinically generated visual questions and answers about radiology images. Sci. Data 5(1), 1\u201310 (2018)","journal-title":"Sci. Data"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"28541","DOI":"10.52202\/075280-1240","volume":"36","author":"C Li","year":"2023","unstructured":"Li, C., et al.: Llava-med: training a large language-and-vision assistant for biomedicine in one day. Adv. Neural. Inf. Process. Syst. 36, 28541\u201328564 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Liao, Z., Hu, S., Zou, K., Fu, H., Zhen, L., Xia, Y.: Vision-amplified semantic entropy for hallucination detection in medical visual question answering. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 669\u2013679. Springer, Heidelberg (2025). DOI: https:\/\/doi.org\/10.1007\/978-3-032-04971-1_63","DOI":"10.1007\/978-3-032-04971-1_63"},{"key":"18_CR14","unstructured":"Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Liu, B., et al.: Gemex: a large-scale, groundable, and explainable medical vqa benchmark for chest x-ray diagnosis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21310\u201321320 (2025)","DOI":"10.1109\/ICCV51701.2025.01979"},{"key":"18_CR16","unstructured":"Liu, H., et al.: A survey on hallucination in large vision-language models. arXiv preprint arXiv:2402.00253 (2024)"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Moslonka, C., Randrianarivo, H., Garnier, A., Malherbe, E.: Learned hallucination detection in black-box llms using token-level entropy production rate. arXiv preprint arXiv:2509.04492 (2025)","DOI":"10.1007\/978-3-032-21289-4_8"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Ostmeier, S., et al.: Green: generative radiology report evaluation and error notation. In: Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 374\u2013390 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.21"},{"key":"18_CR19","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":"18_CR20","doi-asserted-by":"crossref","unstructured":"Raghavan, K., B, S., v, K.: Attention guided grad-cam: an improved explainable artificial intelligence model for infrared breast cancer detection. Multimedia Tools Appl. 83(19), 57551\u201357578 (2024)","DOI":"10.1007\/s11042-023-17776-7"},{"key":"18_CR21","unstructured":"Sellergren, A., et al.: Medgemma technical report. arXiv preprint arXiv:2507.05201 (2025)"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Thawkar, O., et al.: Xraygpt: chest radiographs summarization using medical vision-language models. arXiv preprint arXiv:2306.07971 (2023)","DOI":"10.18653\/v1\/2024.bionlp-1.35"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Xiao, W., et al.: Detecting and mitigating hallucination in large vision language models via fine-grained ai feedback. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 25543\u201325551 (2025)","DOI":"10.1609\/aaai.v39i24.34744"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Xu, W., et\u00a0al.: Lingshu: a generalist foundation model for unified multimodal medical understanding and reasoning. arXiv preprint arXiv:2506.07044 (2025)","DOI":"10.1109\/TPAMI.2026.3730310"},{"key":"18_CR25","unstructured":"Zhang, S., et al.: Radflag: a black-box hallucination detection method for medical vision language models. arXiv preprint arXiv:2411.00299 (2024)"},{"key":"18_CR26","unstructured":"Zhang, S., et\u00a0al.: Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv:2303.00915 (2023)"},{"key":"18_CR27","unstructured":"Zhu, J., et al.: Internvl3: exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479 (2025)"},{"key":"18_CR28","unstructured":"Zou, K., et al.: Medrg: medical report grounding with multi-modal large language model. arXiv e-prints pp. arXiv\u20132404 (2024)"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Zou, K., et\u00a0al.: Uncertainty-aware medical diagnostic phrase identification and grounding. IEEE Trans. Pattern Anal. Mach. Intell. (2025)","DOI":"10.1109\/TPAMI.2025.3596878"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38059-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:06:38Z","timestamp":1789981598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38059-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"ISBN":["9783032380586","9783032380593"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38059-3_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]},"assertion":[{"value":"22 September 2026","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","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}