{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:39:21Z","timestamp":1775540361166,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049261","type":"print"},{"value":"9783032049278","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-04927-8_41","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:08:47Z","timestamp":1758388127000},"page":"429-439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-agent Reasoning for Cardiovascular Imaging Phenotype Analysis"],"prefix":"10.1007","author":[{"given":"Weitong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyun","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqi","family":"Zang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Niederer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul M.","family":"Matthews","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjia","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Sing, C.F., Steng\u00e2rd, J.H., Kardia, S.L.: Genes, environment, and cardiovascular disease. Arteriosclerosis Thrombosis Vascular Biol. 23(7) (2003)","DOI":"10.1161\/01.ATV.0000075081.51227.86"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Cosselman, K.E., Navas-Acien, A., Kaufman, J.D.: Environmental factors in cardiovascular disease. Nature Rev. Cardiol. 12(11) (2015)","DOI":"10.1038\/nrcardio.2015.152"},{"key":"41_CR3","doi-asserted-by":"crossref","unstructured":"Rozanski, A., et al.: Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation (1999)","DOI":"10.1161\/01.CIR.99.16.2192"},{"issue":"01","key":"41_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1055\/s-0031-1298595","volume":"3","author":"AC Skelly","year":"2012","unstructured":"Skelly, A.C., Dettori, J.R., Brodt, E.D.: Assessing bias: the importance of considering confounding. Evidence-based Spine-care J. 3(01), 9\u201312 (2012)","journal-title":"Evidence-based Spine-care J."},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Mukherjee, P., et al.: Confounding factors need to be accounted for in assessing bias by machine learning algorithms. Nature Med. 28(6) (2022)","DOI":"10.1038\/s41591-022-01847-7"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Qiao, M., et al.: CHeart: a conditional spatio-temporal generative model for cardiac anatomy. IEEE Trans. Med. Imaging (2023)","DOI":"10.1007\/978-3-031-80965-1_15"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Qiao, M., et al.: A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics. Nature Mach. Intell., 1\u201312 (2025)","DOI":"10.21203\/rs.3.rs-5124497\/v1"},{"key":"41_CR8","unstructured":"Touvron, H., Lavril, T., et al.: Llama: Open and efficient foundation language models. ArXiv preprint. 2023;abs\/2302.13971"},{"key":"41_CR9","unstructured":"OpenAI. GPT-4 Technical report. ArXiv preprint. 2023;abs\/2303.08774"},{"key":"41_CR10","unstructured":"Lu, P., et al.: Chameleon: Plug-and-play compositional reasoning with large language models. arXiv preprint arXiv:2304.09842 (2023)"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Park, J.S, et al.: Generative agents: interactive simulacra of human behavior. In: UIST. Association for Computing Machinery (2023)","DOI":"10.1145\/3586183.3606763"},{"key":"41_CR12","unstructured":"Zhang, W., Zang, C., Schmidt, M., Blythman, R.: BiD: Behavioral agents in dynamic auctions (2024)"},{"key":"41_CR13","unstructured":"Zhang, W., Zang, C., Kainz, B.: Truth or deceit? A Bayesian decoding game enhances consistency and reliability (2024)"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature. 616(7956) (2023)","DOI":"10.1038\/s41586-023-05881-4"},{"key":"41_CR15","first-page":"620","volume":"07","author":"K Singhal","year":"2023","unstructured":"Singhal, K., Azizi, S., Tu, T., Mahdavi, S., Wei, J., et al.: Large language models encode clinical knowledge. Nature 07, 620 (2023)","journal-title":"Nature"},{"key":"41_CR16","unstructured":"Zhang, X., et al.: AlpaCare: Instruction-tuned Large Language Models for Medical Application (2023)"},{"key":"41_CR17","unstructured":"Bao, Z, et al.: DISC-MedLLM: Bridging general large language models and real-world medical consultation (2023)"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Bai, W., et\u00a0al.: A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Med. 26(10) (2020)","DOI":"10.1038\/s41591-020-1009-y"},{"key":"41_CR19","unstructured":"Li\u00e9vin, V., Hother, C.E., Winther, O.: Can large language models reason about medical questions? arXiv preprint arXiv:2207.08143 (2022)"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Schmidt, H.G., Rikers, R.M.: How expertise develops in medicine: knowledge encapsulation and illness script formation. Med. Educ. 41(12) (2007)","DOI":"10.1111\/j.1365-2923.2007.02915.x"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Tarale, P., Rietman, E., Siegelmann, H.T.: Distributed multi-agent lifelong learning. Trans. Mach. Learn. Res. (2025)","DOI":"10.24135\/ICONIP17"},{"key":"41_CR22","unstructured":"Zheng, G., et al.: Towards a collective medical imaging AI: enabling continual learning from peers. In: MIDL (2024)"},{"key":"41_CR23","doi-asserted-by":"crossref","unstructured":"Harris, E.: Large language models answer medical questions accurately, but can\u2019t match clinicians\u2019 knowledge. JAMA (2023)","DOI":"10.1001\/jama.2023.14311"},{"issue":"12","key":"41_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571730","volume":"55","author":"Z Ji","year":"2023","unstructured":"Ji, Z.: Survey of hallucination in natural language generation. ACM Comput. Surv. 55(12), 1\u201338 (2023)","journal-title":"ACM Comput. Surv."},{"key":"41_CR25","doi-asserted-by":"crossref","unstructured":"Singhal, K., et\u00a0al.: Large language models encode clinical knowledge. Nature. 620(7972) (2023)","DOI":"10.1038\/s41586-023-06291-2"},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Tang, X., et\u00a0al.: Medagents: Large language models as collaborators for zero-shot medical reasoning. arXiv preprint arXiv:2311.10537 (2023)","DOI":"10.18653\/v1\/2024.findings-acl.33"},{"key":"41_CR27","unstructured":"Chen X, et al.: RareAgents: Autonomous multi-disciplinary team for rare disease diagnosis and treatment. arXiv:2412.12475 (2024)"},{"key":"41_CR28","doi-asserted-by":"crossref","unstructured":"Lu, M., Ho, B., Ren, D., Wang, X.: TriageAgent: Towards better multi-agents collaborations for large language model-based clinical triage. In: Findings of the Association for Computational Linguistics: EMNLP, p. 5747-64 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.329"},{"key":"41_CR29","doi-asserted-by":"crossref","unstructured":"Li, B., et\u00a0al.: MMedAgent: Learning to use medical tools with multi-modal agent. arXiv:2407.02483 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.510"},{"key":"41_CR30","unstructured":"Li, Y., Zhang, Y., Sun, L.: MetaAgents: Simulating interactions of human behaviors for LLM-based task-oriented coordination via collaborative generative agents. arXiv:2310.06500 (2023)"},{"key":"41_CR31","doi-asserted-by":"crossref","unstructured":"Wang Z, et al.: Unleashing the emergent cognitive synergy in large language models: a task-solving agent through multi-persona self-collaboration. NAACL (2024)","DOI":"10.18653\/v1\/2024.naacl-long.15"},{"key":"41_CR32","unstructured":"Wu, Q., et\u00a0al.: AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework. arXiv:2308.08155 (2023)"},{"key":"41_CR33","unstructured":"Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. In: ICML (2023)"},{"key":"41_CR34","unstructured":"Fu, Y., Peng, H., Khot, T., Lapata, M.: Improving language model negotiation with self-play and in-context learning from AI feedback. arXiv:2305.10142 (2023)"},{"key":"41_CR35","unstructured":"Yang, Z., et\u00a0al. Oasis: Open agents social interaction simulations on one million agents. arXiv preprint arXiv:2411.11581 (2024)"},{"key":"41_CR36","unstructured":"Achiam, J., et\u00a0al.: GPT-4 Technical Report. arXiv:2303.08774 (2023)"},{"key":"41_CR37","unstructured":"Anthropic. Claude 3.5 (2023). https:\/\/www.anthropic.com\/claude"},{"key":"41_CR38","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, X., et al.: Chain-of-thought prompting elicits reasoning in large language models. NeurIPS. 35, 24824\u201337 (2022)","journal-title":"NeurIPS."},{"issue":"6","key":"41_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235145","volume":"15","author":"M Strocchi","year":"2020","unstructured":"Strocchi, M.: A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations. PLoS ONE 15(6), e0235145 (2020)","journal-title":"PLoS ONE"},{"issue":"1","key":"41_CR40","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u201339 (1997)","journal-title":"J. Comput. Syst. Sci."},{"issue":"2","key":"41_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179\u201388 (1936)","journal-title":"Ann. Eugen."},{"key":"41_CR42","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u201397 (1995)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04927-8_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:08:57Z","timestamp":1758388137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04927-8_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049261","9783032049278"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04927-8_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 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":"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (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":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}