{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T07:25:57Z","timestamp":1779002757057,"version":"3.51.4"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049773","type":"print"},{"value":"9783032049780","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-04978-0_36","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:16:19Z","timestamp":1758212179000},"page":"373-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge Tree Driven Contextualized Instruction Tuning of\u00a0Foundation Models for\u00a0Epilepsy Drug Recommendation"],"prefix":"10.1007","author":[{"given":"Duy Khoa","family":"Pham","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deval","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwen","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Thom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard Shek-kwan","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Nazem-Zadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Foster","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy","family":"Fazio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Holper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karin","family":"Verspoor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duong","family":"Nhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Barnard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terence","family":"O\u2019Brien","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacqueline","family":"French","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Kwan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"issue":"6","key":"36_CR1","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1111\/epi.15612","volume":"60","author":"A Bernasconi","year":"2019","unstructured":"Bernasconi, A., et al.: Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the international league against epilepsy neuroimaging task force. Epilepsia 60(6), 1054\u20131068 (2019)","journal-title":"Epilepsia"},{"key":"36_CR2","doi-asserted-by":"publisher","unstructured":"Chen, L., et al.: ShareGPT4V: improving large multi-modal models with better captions. In: European Conference on Computer Vision, pp. 370\u2013387. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72643-9_22","DOI":"10.1007\/978-3-031-72643-9_22"},{"issue":"2","key":"36_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1097\/WCO.0000000000000803","volume":"33","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Brodie, M.J., Kwan, P.: What has been the impact of new drug treatments on epilepsy? Curr. Opin. Neurol. 33(2), 185\u2013190 (2020)","journal-title":"Curr. Opin. Neurol."},{"issue":"3","key":"36_CR4","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1001\/jamaneurol.2017.3949","volume":"75","author":"Z Chen","year":"2018","unstructured":"Chen, Z., Brodie, M.J., Liew, D., Kwan, P.: Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: a 30-year longitudinal cohort study. JAMA Neurol. 75(3), 279\u2013286 (2018)","journal-title":"JAMA Neurol."},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Brain health: new era of personalised epilepsy management. The BMJ 371 (2020)","DOI":"10.1136\/bmj.m3658"},{"issue":"5","key":"36_CR6","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.1007\/s00415-023-11603-7","volume":"270","author":"M Cheval","year":"2023","unstructured":"Cheval, M., et al.: Early identification of seizure freedom with medical treatment in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. J. Neurol. 270(5), 2715\u20132723 (2023)","journal-title":"J. Neurol."},{"key":"36_CR7","unstructured":"Chiang, W.L., et\u00a0al.: Vicuna: an open-source chatbot impressing GPT-4 with 90%* ChatGPT quality. 2(3), 6 (2023). https:\/\/vicuna.lmsys.org. Accessed 14 Apr 2023"},{"issue":"12","key":"36_CR8","doi-asserted-by":"publisher","first-page":"3035","DOI":"10.1016\/j.clinph.2021.08.024","volume":"132","author":"P Croce","year":"2021","unstructured":"Croce, P., et al.: Machine learning for predicting levetiracetam treatment response in temporal lobe epilepsy. Clin. Neurophysiol. 132(12), 3035\u20133042 (2021)","journal-title":"Clin. Neurophysiol."},{"issue":"12","key":"36_CR9","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.1111\/epi.17802","volume":"64","author":"J Fox","year":"2023","unstructured":"Fox, J., et al.: Patterns of antiseizure medication utilization in the human epilepsy project. Epilepsia 64(12), 3196\u20133204 (2023)","journal-title":"Epilepsia"},{"issue":"10","key":"36_CR10","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1212\/WNL.0b013e3182a35193","volume":"81","author":"T Hakami","year":"2013","unstructured":"Hakami, T., et al.: MRI-identified pathology in adults with new-onset seizures. Neurology 81(10), 920\u2013927 (2013)","journal-title":"Neurology"},{"issue":"10","key":"36_CR11","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1001\/jamaneurol.2022.2514","volume":"79","author":"H Hakeem","year":"2022","unstructured":"Hakeem, H., et al.: Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy. JAMA Neurol. 79(10), 986\u2013996 (2022)","journal-title":"JAMA Neurol."},{"issue":"7","key":"36_CR12","doi-asserted-by":"publisher","first-page":"853","DOI":"10.3174\/ajnr.A7911","volume":"44","author":"Z Hu","year":"2023","unstructured":"Hu, Z., et al.: Predicting drug treatment outcomes in children with tuberous sclerosis complex-related epilepsy: a clinical radiomics study. Am. J. Neuroradiol. 44(7), 853\u2013860 (2023)","journal-title":"Am. J. Neuroradiol."},{"issue":"20","key":"36_CR13","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1212\/WNL.0000000000002674","volume":"86","author":"A Labate","year":"2016","unstructured":"Labate, A., et al.: Long-term outcome of mild mesial temporal lobe epilepsy: a prospective longitudinal cohort study. Neurology 86(20), 1904\u20131910 (2016)","journal-title":"Neurology"},{"issue":"3","key":"36_CR14","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1093\/brain\/awab425","volume":"145","author":"HM Lee","year":"2022","unstructured":"Lee, H.M., et al.: Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification. Brain 145(3), 897\u2013908 (2022)","journal-title":"Brain"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: LLaVA-Med: training a large language-and-vision assistant for biomedicine in one day. In: Advances in Neural Information Processing Systems, vol. 36 (2024)","DOI":"10.32388\/VLXB6M"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26296\u201326306 (2024)","DOI":"10.1109\/CVPR52733.2024.02484"},{"key":"36_CR17","first-page":"34892","volume":"36","author":"H Liu","year":"2023","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Adv. Neural. Inf. Process. Syst. 36, 34892\u201334916 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"36_CR18","unstructured":"Maziarz, K., et al.: Learning to extend molecular scaffolds with structural motifs. arXiv preprint arXiv:2103.03864 (2021)"},{"issue":"6","key":"36_CR19","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/S1474-4422(20)30035-1","volume":"19","author":"E Perucca","year":"2020","unstructured":"Perucca, E., Brodie, M.J., Kwan, P., Tomson, T.: 30 years of second-generation antiseizure medications: impact and future perspectives. The Lancet Neurol. 19(6), 544\u2013556 (2020)","journal-title":"The Lancet Neurol."},{"key":"36_CR20","unstructured":"Sepehri, M.S., Fabian, Z., Soltanolkotabi, M., Soltanolkotabi, M.: MediConfusion: can you trust your ai radiologist? probing the reliability of multimodal medical foundation models. arXiv preprint arXiv:2409.15477 (2024)"},{"issue":"10","key":"36_CR21","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1016\/j.eplepsyres.2014.08.022","volume":"108","author":"K Shazadi","year":"2014","unstructured":"Shazadi, K., et al.: Validation of a multigenic model to predict seizure control in newly treated epilepsy. Epilepsy Res. 108(10), 1797\u20131805 (2014)","journal-title":"Epilepsy Res."},{"issue":"1","key":"36_CR22","doi-asserted-by":"publisher","first-page":"22532","DOI":"10.1038\/s41598-023-49255-2","volume":"13","author":"Y Shin","year":"2023","unstructured":"Shin, Y., et al.: Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning. Sci. Rep. 13(1), 22532 (2023)","journal-title":"Sci. Rep."},{"issue":"1","key":"36_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169214","volume":"12","author":"MS Silva-Alves","year":"2017","unstructured":"Silva-Alves, M.S., et al.: A prediction algorithm for drug response in patients with mesial temporal lobe epilepsy based on clinical and genetic information. PLoS ONE 12(1), e0169214 (2017)","journal-title":"PLoS ONE"},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.jocn.2021.07.016","volume":"91","author":"X Wang","year":"2021","unstructured":"Wang, X., Hu, T., Yang, Q., Jiao, D., Yan, Y., Liu, L.: Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy. J. Clin. Neurosci. 91, 276\u2013282 (2021)","journal-title":"J. Clin. Neurosci."},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31\u201336 (1988)","DOI":"10.1021\/ci00057a005"},{"key":"36_CR26","unstructured":"World Health Organization: Epilepsy (2024). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/epilepsy. Accessed 13 Feb 2025"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Xiong, Z., et al.: How generalizable are foundation models when applied to different demographic groups and settings? NEJM AI 2(1), AIcs2400497 (2025)","DOI":"10.1056\/AIcs2400497"},{"key":"36_CR28","unstructured":"Zhang, S., et\u00a0al.: BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915 (2023)"}],"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\/978-3-032-04978-0_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:07:35Z","timestamp":1758233255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 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"}}]}}