{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T18:19:55Z","timestamp":1774462795937,"version":"3.50.1"},"reference-count":225,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"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":["Vis Comput"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s00371-026-04385-2","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:51:49Z","timestamp":1771681909000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large language models in radiogenomics: a comprehensive survey of applications from imaging to genetics"],"prefix":"10.1007","volume":"42","author":[{"given":"Muhammad Nadeem","family":"Cheema","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anam","family":"Nazir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arif","family":"Harmanci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akdes Serin","family":"Harmanci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasmeen","family":"Cheema","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saleha","family":"Masood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahad Ahmed","family":"Khokhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"issue":"7571","key":"4385_CR1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nature15393","volume":"526","author":"A Auton","year":"2015","unstructured":"Auton, A., et al.: A global reference for human genetic variation. Nature 526(7571), 68\u201374 (2015). https:\/\/doi.org\/10.1038\/nature15393","journal-title":"Nature"},{"key":"4385_CR2","unstructured":"M. A. Qazi et al., (2024) Continual learning in medical imaging: a survey and practical analysis"},{"issue":"10","key":"4385_CR3","doi-asserted-by":"publisher","first-page":"450","DOI":"10.5694\/mja2.51077","volume":"214","author":"M Law","year":"2021","unstructured":"Law, M., Seah, J., Shih, G.: Artificial intelligence and medical imaging: applications, challenges and solutions. Med. J. Aust. 214(10), 450 (2021). https:\/\/doi.org\/10.5694\/mja2.51077","journal-title":"Med. J. Aust."},{"issue":"3","key":"4385_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2024.100929","volume":"5","author":"B Qian","year":"2024","unstructured":"Qian, B., et al.: DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. Patterns 5(3), 100929 (2024). https:\/\/doi.org\/10.1016\/j.patter.2024.100929","journal-title":"Patterns"},{"issue":"8067","key":"4385_CR5","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1038\/s41586-025-08869-4","volume":"642","author":"D McDuff","year":"2025","unstructured":"McDuff, D., et al.: Towards accurate differential diagnosis with large language models. Nature 642(8067), 451\u2013457 (2025). https:\/\/doi.org\/10.1038\/s41586-025-08869-4","journal-title":"Nature"},{"issue":"1","key":"4385_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/s41746-023-00989-3","volume":"7","author":"H Wang","year":"2024","unstructured":"Wang, H., Gao, C., Dantona, C., Hull, B., Sun, J.: DRG-LLaMA\u202f: tuning LLaMA model to predict diagnosis-related group for hospitalized patients. NPJ Digit Med 7(1), 16 (2024). https:\/\/doi.org\/10.1038\/s41746-023-00989-3","journal-title":"NPJ Digit Med"},{"key":"4385_CR7","doi-asserted-by":"publisher","unstructured":"H. Dalla-Torre et al., (2023) \u201cThe nucleotide transformer: building and evaluating robust foundation models for human genomics, https:\/\/doi.org\/10.1101\/2023.01.11.523679.","DOI":"10.1101\/2023.01.11.523679"},{"key":"4385_CR8","unstructured":"H. Jin, L. Huang, H. Cai, J. Yan, B. Li, and H. Chen, \u201cFrom LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future,\u201d Apr. 2025."},{"issue":"12","key":"4385_CR9","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1038\/s42256-024-00944-1","volume":"6","author":"J Qiu","year":"2024","unstructured":"Qiu, J., et al.: LLM-based agentic systems in medicine and healthcare. Nat Mach Intell 6(12), 1418\u20131420 (2024). https:\/\/doi.org\/10.1038\/s42256-024-00944-1","journal-title":"Nat Mach Intell"},{"key":"4385_CR10","doi-asserted-by":"publisher","DOI":"10.1093\/bioadv\/vbaf019","author":"S Lu","year":"2024","unstructured":"Lu, S., Cosgun, E.: Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and fine-tuning. Bioinform. Adv. (2024). https:\/\/doi.org\/10.1093\/bioadv\/vbaf019","journal-title":"Bioinform. Adv."},{"issue":"2","key":"4385_CR11","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1097\/CM9.0000000000003456","volume":"138","author":"X Yang","year":"2025","unstructured":"Yang, X., et al.: Application of large language models in disease diagnosis and treatment. Chin Med J (Engl) 138(2), 130\u2013142 (2025). https:\/\/doi.org\/10.1097\/CM9.0000000000003456","journal-title":"Chin Med J (Engl)"},{"issue":"11","key":"4385_CR12","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1038\/s41591-018-0213-5","volume":"24","author":"M Komorowski","year":"2018","unstructured":"Komorowski, M., Celi, L.A., Badawi, O., Gordon, A.C., Faisal, A.A.: The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat. Med. 24(11), 1716\u20131720 (2018). https:\/\/doi.org\/10.1038\/s41591-018-0213-5","journal-title":"Nat. Med."},{"issue":"9","key":"4385_CR13","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1021\/acs.jcim.1c00600","volume":"62","author":"V Bagal","year":"2022","unstructured":"Bagal, V., Aggarwal, R., Vinod, P.K., Priyakumar, U.D.: MolGPT: molecular generation using a transformer-decoder model. J. Chem. Inf. Model. 62(9), 2064\u20132076 (2022). https:\/\/doi.org\/10.1021\/acs.jcim.1c00600","journal-title":"J. Chem. Inf. Model."},{"key":"4385_CR14","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac131","author":"Z Wu","year":"2022","unstructured":"Wu, Z., et al.: Knowledge-based BERT: a method to extract molecular features like computational chemists. Brief. Bioinform. (2022). https:\/\/doi.org\/10.1093\/bib\/bbac131","journal-title":"Brief. Bioinform."},{"issue":"1","key":"4385_CR15","doi-asserted-by":"publisher","first-page":"114","DOI":"10.3390\/biom14010114","volume":"14","author":"Y Cheema","year":"2024","unstructured":"Cheema, Y., Linton, K.J., Jabeen, I.: molecular modeling studies to probe the binding hypothesis of novel lead compounds against multidrug resistance protein ABCB1. Biomolecules 14(1), 114 (2024). https:\/\/doi.org\/10.3390\/biom14010114","journal-title":"Biomolecules"},{"key":"4385_CR16","doi-asserted-by":"publisher","DOI":"10.1093\/bjrai\/ubae007","author":"E Sizikova","year":"2024","unstructured":"Sizikova, E., et al.: Synthetic data in radiological imaging: current state and future outlook. BJR|Artificial Intelligence (2024). https:\/\/doi.org\/10.1093\/bjrai\/ubae007","journal-title":"BJR|Artificial Intelligence"},{"issue":"6","key":"4385_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2022","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1\u201335 (2022). https:\/\/doi.org\/10.1145\/3457607","journal-title":"ACM Comput. Surv."},{"key":"4385_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-025-04061-x","author":"MN Cheema","year":"2025","unstructured":"Cheema, M.N., Zhang, L., Nazir, A., Li, Y., Detre, J.A., Wang, Z.: Transformer-based arterial spin labeling perfusion MRI denoising. Vis. Comput. (2025). https:\/\/doi.org\/10.1007\/s00371-025-04061-x","journal-title":"Vis. Comput."},{"issue":"1","key":"4385_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-023-04203-7","volume":"27","author":"D Myers","year":"2024","unstructured":"Myers, D., et al.: Foundation and large language models: fundamentals, challenges, opportunities, and social impacts. Cluster Comput 27(1), 1\u201326 (2024). https:\/\/doi.org\/10.1007\/s10586-023-04203-7","journal-title":"Cluster Comput"},{"key":"4385_CR20","unstructured":"Y. Han, X. Liu, X. Zhang, and C. Ding, (2024) foundation models in electrocardiogram: a review"},{"key":"4385_CR21","unstructured":"H. Touvron et al., (2023) LLaMA: open and efficient foundation language models"},{"key":"4385_CR22","unstructured":"R. Bommasani et al., (2022) On the opportunities and risks of foundation Models"},{"issue":"1","key":"4385_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-024-00414-9","volume":"18","author":"X Li","year":"2025","unstructured":"Li, X., Peng, L., Wang, Y.-P., Zhang, W.: Open challenges and opportunities in federated foundation models towards biomedical healthcare. BioData Min. 18(1), 2 (2025). https:\/\/doi.org\/10.1186\/s13040-024-00414-9","journal-title":"BioData Min."},{"issue":"6","key":"4385_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24065298","volume":"24","author":"Y Cheema","year":"2023","unstructured":"Cheema, Y., Kiani, Y.S., Linton, K.J., Jabeen, I.: Identification and empiric evaluation of new inhibitors of the multidrug transporter P-glycoprotein (ABCB1). Int. J. Mol. Sci. 24(6), 5298 (2023). https:\/\/doi.org\/10.3390\/ijms24065298","journal-title":"Int. J. Mol. Sci."},{"key":"4385_CR25","doi-asserted-by":"crossref","unstructured":"Z. Chen, L. Luo, Y. Bie, and H. Chen, (2024) Dia-LLaMA: Towards large language model-driven CT report generation","DOI":"10.1007\/978-3-032-04981-0_14"},{"key":"4385_CR26","unstructured":"OpenAI et al., (2024) GPT-4 Technical report"},{"key":"4385_CR27","doi-asserted-by":"crossref","unstructured":"Y. Kim, J. Wu, Y. Abdulle, and H. Wu, (2024) MedExQA: Medical question answering benchmark with multiple explanations","DOI":"10.18653\/v1\/2024.bionlp-1.14"},{"key":"4385_CR28","doi-asserted-by":"publisher","unstructured":"Y.-P. Lee, S. Brooks, and C. Gilbert, (2024) Large language models for traditional chinese medicine question answering. https:\/\/doi.org\/10.21203\/rs.3.rs-5671664\/v1.","DOI":"10.21203\/rs.3.rs-5671664\/v1"},{"issue":"2","key":"4385_CR29","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/s11604-023-01487-y","volume":"42","author":"T Nakaura","year":"2024","unstructured":"Nakaura, T., et al.: Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports. Jpn. J. Radiol. 42(2), 190\u2013200 (2024). https:\/\/doi.org\/10.1007\/s11604-023-01487-y","journal-title":"Jpn. J. Radiol."},{"key":"4385_CR30","doi-asserted-by":"publisher","unstructured":"A. S. Wahd et al., (2024) \u201cSam2Rad: a segmentation model for medical images with learnable prompts, https:\/\/doi.org\/10.1016\/j.compbiomed.2025.109725.","DOI":"10.1016\/j.compbiomed.2025.109725"},{"key":"4385_CR31","doi-asserted-by":"publisher","unstructured":"A. Kirillov et al., (2023) Segment anything, in 2023 IEEE\/CVF International conference on computer vision (ICCV), IEEE, pp. 3992\u20134003. https:\/\/doi.org\/10.1109\/ICCV51070.2023.00371.","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"1","key":"4385_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00742-2","volume":"5","author":"X Yang","year":"2022","unstructured":"Yang, X., et al.: A large language model for electronic health records. NPJ Digit. Med. 5(1), 194 (2022). https:\/\/doi.org\/10.1038\/s41746-022-00742-2","journal-title":"NPJ Digit. Med."},{"key":"4385_CR33","unstructured":"X. Yang et al., (2022) GatorTron: a large clinical language model to unlock patient information from unstructured electronic health records"},{"issue":"8","key":"4385_CR34","doi-asserted-by":"publisher","first-page":"5625","DOI":"10.1109\/TPAMI.2024.3369699","volume":"46","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Huang, J., Jin, S., Lu, S.: Vision-language models for vision tasks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46(8), 5625\u20135644 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3369699","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4385_CR35","doi-asserted-by":"crossref","unstructured":"Y. Du, Z. Liu, J. Li, and W. X. Zhao, (2022) A survey of vision-language pre-trained models","DOI":"10.24963\/ijcai.2022\/762"},{"key":"4385_CR36","doi-asserted-by":"publisher","unstructured":"Z. Wang, Z. Wu, D. Agarwal, and J. Sun, (2022) \u201cMedCLIP: Contrastive learning from unpaired medical images and text, in proceedings of the 2022 conference on empirical methods in natural language processing, Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 3876\u20133887. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.256.","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"4385_CR37","unstructured":"Z. Yang, S. S. Batra, J. Stremmel, and E. Halperin, (2023) \u201cSurpassing GPT-4 medical coding with a two-stage approach"},{"key":"4385_CR38","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.18653\/v1\/2023.findings-emnlp.123","volume":"2023","author":"Z. Ji, T. Yu, Y. Xu, N. Lee, E. Ishii, P. Fung, \u201cTowards Mitigating LLM Hallucination via Self Reflection\u201d, in Findings of the Association for Computational Linguistics: EMNLP","year":"2023","unstructured":"Z. Ji, T. Yu, Y. Xu, N. Lee, E. Ishii, P. Fung, \u201cTowards Mitigating LLM Hallucination via Self Reflection\u201d, in Findings of the Association for Computational Linguistics: EMNLP: Stroudsburg, PA, USA: association for. Comput. Linguist. 2023, 1827\u20131843 (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.123","journal-title":"Comput. Linguist."},{"issue":"7","key":"4385_CR39","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1001\/jamapsychiatry.2023.1253","volume":"80","author":"AC van Heerden","year":"2023","unstructured":"van Heerden, A.C., Pozuelo, J.R., Kohrt, B.A.: Global mental health services and the impact of artificial intelligence\u2013powered large language models. JAMA Psychiat. 80(7), 662 (2023). https:\/\/doi.org\/10.1001\/jamapsychiatry.2023.1253","journal-title":"JAMA Psychiat."},{"key":"4385_CR40","doi-asserted-by":"crossref","unstructured":"D. Jin, E. Pan, N. Oufattole, W.-H. Weng, H. Fang, and P. Szolovits, (2020) What disease does this patient have? A large-scale open domain question answering dataset from medical exams","DOI":"10.20944\/preprints202105.0498.v1"},{"issue":"6637","key":"4385_CR41","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin, Z., et al.: Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637), 1123\u20131130 (2023). https:\/\/doi.org\/10.1126\/science.ade2574","journal-title":"Science"},{"key":"4385_CR42","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad718","author":"Y Fang","year":"2023","unstructured":"Fang, Y., et al.: DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model. Bioinformatics (2023). https:\/\/doi.org\/10.1093\/bioinformatics\/btad718","journal-title":"Bioinformatics"},{"issue":"15","key":"4385_CR43","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1093\/bioinformatics\/btab083","volume":"37","author":"Y Ji","year":"2021","unstructured":"Ji, Y., Zhou, Z., Liu, H., Davuluri, R.V.: DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome. Bioinformatics 37(15), 2112\u20132120 (2021). https:\/\/doi.org\/10.1093\/bioinformatics\/btab083","journal-title":"Bioinformatics"},{"issue":"2","key":"4385_CR44","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/s00371-024-03371-w","volume":"41","author":"Y Cheema","year":"2025","unstructured":"Cheema, Y., Cheema, M.N., Nazir, A., Khokhar, F.A., Li, P., Ahmed, A.: A novel approach for improving open scene text translation with modified GAN. Vis. Comput. 41(2), 869\u2013881 (2025). https:\/\/doi.org\/10.1007\/s00371-024-03371-w","journal-title":"Vis. Comput."},{"issue":"8","key":"4385_CR45","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1109\/TBME.2018.2884319","volume":"66","author":"MN Cheema","year":"2019","unstructured":"Cheema, M.N., et al.: Image-aligned dynamic liver reconstruction using intra-operative field of views for minimal invasive surgery. IEEE Trans. Biomed. Eng. 66(8), 2163\u20132173 (2019). https:\/\/doi.org\/10.1109\/TBME.2018.2884319","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"4385_CR46","doi-asserted-by":"publisher","first-page":"7192","DOI":"10.1109\/TIP.2020.2999854","volume":"29","author":"A Nazir","year":"2020","unstructured":"Nazir, A., et al.: OFF-eNET: an optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation. IEEE Trans. Image Process. 29, 7192\u20137202 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2999854","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"4385_CR47","doi-asserted-by":"publisher","first-page":"2641","DOI":"10.1109\/TBME.2019.2894123","volume":"66","author":"MN Cheema","year":"2019","unstructured":"Cheema, M.N., Nazir, A., Sheng, B., Li, P., Qin, J., Feng, D.D.: Liver extraction using residual convolution neural networks from low-dose CT images. IEEE Trans. Biomed. Eng. 66(9), 2641\u20132650 (2019). https:\/\/doi.org\/10.1109\/TBME.2019.2894123","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"4385_CR48","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TIP.2021.3136619","volume":"31","author":"A Nazir","year":"2022","unstructured":"Nazir, A., et al.: ECSU-Net: an embedded clustering sliced U-net coupled with fusing strategy for efficient intervertebral disc segmentation and classification. IEEE Trans. Image Process. 31, 880\u2013893 (2022). https:\/\/doi.org\/10.1109\/TIP.2021.3136619","journal-title":"IEEE Trans. Image Process."},{"key":"4385_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103430","volume":"106","author":"A Nazir","year":"2020","unstructured":"Nazir, A., et al.: SPST-CNN: spatial pyramid based searching and tagging of liver\u2019s intraoperative live views via CNN for minimal invasive surgery. J. Biomed. Inform. 106, 103430 (2020). https:\/\/doi.org\/10.1016\/j.jbi.2020.103430","journal-title":"J. Biomed. Inform."},{"key":"4385_CR50","unstructured":"A. Vaswani et al., (2023) Attention is all you need"},{"key":"4385_CR51","unstructured":"P. He, X. Liu, J. Gao, and W. Chen, (2021) DeBERTa: decoding-enhanced BERT with disentangled attention"},{"key":"4385_CR52","doi-asserted-by":"crossref","unstructured":"H. Zhao et al., (2024) Looking beyond text: reducing language bias in large vision-language models via multimodal dual-attention and soft-image guidance","DOI":"10.18653\/v1\/2025.emnlp-main.995"},{"key":"4385_CR53","doi-asserted-by":"crossref","unstructured":"E. Alsentzer et al., (2019) Publicly available clinical BERT Embeddings","DOI":"10.18653\/v1\/W19-1909"},{"key":"4385_CR54","doi-asserted-by":"publisher","unstructured":"D. Zhang et al., (2023) DNAGPT: a generalized pre-trained tool for multiple dna sequence analysis tasks, https:\/\/doi.org\/10.1101\/2023.07.11.548628.","DOI":"10.1101\/2023.07.11.548628"},{"issue":"10s","key":"4385_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. 54(10s), 1\u201341 (2022). https:\/\/doi.org\/10.1145\/3505244","journal-title":"ACM Comput. Surv."},{"key":"4385_CR56","doi-asserted-by":"publisher","unstructured":"A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, (2022) Swin UNETR: swin transformers for semantic segmentation of brain tumors in\u00a0mri images, pp. 272\u2013284. https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"4385_CR57","doi-asserted-by":"crossref","unstructured":"Q. Niu et al., (2024) From text to multimodality: exploring the evolution and impact of large language models in medical practice","DOI":"10.31219\/osf.io\/7am8k"},{"key":"4385_CR58","doi-asserted-by":"publisher","unstructured":"Y. Luo et al., (2024) \u201cBioMedGPT: An open multimodal large language model for bioMedicine,\u201d IEEE J Biomed Health Inform, pp. 1\u201312, https:\/\/doi.org\/10.1109\/JBHI.2024.3505955.","DOI":"10.1109\/JBHI.2024.3505955"},{"key":"4385_CR59","unstructured":"Z. Liu et al., (2023) \u201cRadiology-Llama2: best-in-class large language model for radiology"},{"key":"4385_CR60","unstructured":"M. Moor et al., (2023) Med-flamingo: a multimodal medical few-shot learner"},{"key":"4385_CR61","unstructured":"S. L. Hyland et al., (2024) MAIRA-1: A specialised large multimodal model for radiology report generation"},{"key":"4385_CR62","doi-asserted-by":"publisher","DOI":"10.1093\/nargab\/lqac012","author":"M Akiyama","year":"2022","unstructured":"Akiyama, M., Sakakibara, Y.: Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning. NAR Genomics Bioinform. (2022). https:\/\/doi.org\/10.1093\/nargab\/lqac012","journal-title":"NAR Genomics Bioinform."},{"key":"4385_CR63","doi-asserted-by":"publisher","first-page":"5676","DOI":"10.1016\/j.csbj.2023.11.025","volume":"21","author":"MF Danilevicz","year":"2023","unstructured":"Danilevicz, M.F., et al.: Dnabert-based explainable lncRNA identification in plant genome assemblies. Comput. Struct. Biotechnol. J. 21, 5676\u20135685 (2023). https:\/\/doi.org\/10.1016\/j.csbj.2023.11.025","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"6","key":"4385_CR64","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1038\/ng.2653","volume":"45","author":"J Lonsdale","year":"2013","unstructured":"Lonsdale, J., et al.: The genotype-tissue expression (GTEx) project. Nat. Genet. 45(6), 580\u2013585 (2013). https:\/\/doi.org\/10.1038\/ng.2653","journal-title":"Nat. Genet."},{"issue":"1","key":"4385_CR65","doi-asserted-by":"publisher","first-page":"D138","DOI":"10.1093\/nar\/gkad965","volume":"52","author":"E Clough","year":"2024","unstructured":"Clough, E., et al.: NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res. 52(1), D138\u2013D144 (2024). https:\/\/doi.org\/10.1093\/nar\/gkad965","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"4385_CR66","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32007-7","volume":"13","author":"N Ferruz","year":"2022","unstructured":"Ferruz, N., Schmidt, S., H\u00f6cker, B.: ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13(1), 4348 (2022). https:\/\/doi.org\/10.1038\/s41467-022-32007-7","journal-title":"Nat. Commun."},{"issue":"1","key":"4385_CR67","doi-asserted-by":"publisher","first-page":"i255","DOI":"10.1093\/bioinformatics\/btaf188","volume":"41","author":"Z Zhou","year":"2025","unstructured":"Zhou, Z., et al.: DNABERT-S: pioneering species differentiation with species-aware DNA embeddings. Bioinformatics 41(1), i255\u2013i264 (2025). https:\/\/doi.org\/10.1093\/bioinformatics\/btaf188","journal-title":"Bioinformatics"},{"key":"4385_CR68","unstructured":"E. Bolton et al., (2024) BioMedLM: A 2.7B parameter language model trained on biomedical text"},{"key":"4385_CR69","doi-asserted-by":"crossref","unstructured":"X. Tang et al., (2024) MedAgents: large language models as collaborators for zero-shot medical reasoning","DOI":"10.18653\/v1\/2024.findings-acl.33"},{"issue":"4","key":"4385_CR70","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). https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","journal-title":"Bioinformatics"},{"key":"4385_CR71","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac409","author":"R Luo","year":"2022","unstructured":"Luo, R., et al.: BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform. (2022). https:\/\/doi.org\/10.1093\/bib\/bbac409","journal-title":"Brief. Bioinform."},{"key":"4385_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102861","volume":"151","author":"P Esmaeilzadeh","year":"2024","unstructured":"Esmaeilzadeh, P.: Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: a perspective for healthcare organizations. Artif. Intell. Med. 151, 102861 (2024). https:\/\/doi.org\/10.1016\/j.artmed.2024.102861","journal-title":"Artif. Intell. Med."},{"key":"4385_CR73","unstructured":"C. Christophe et al., (2024) \u201cMed42-evaluating fine-tuning strategies for medical LLMs: full-parameter vs. parameter-efficient approaches"},{"issue":"11","key":"4385_CR74","doi-asserted-by":"publisher","first-page":"3755","DOI":"10.1109\/TMI.2024.3398350","volume":"43","author":"Z Zhao","year":"2024","unstructured":"Zhao, Z., et al.: ChatCAD+: toward a universal and reliable interactive CAD using LLMs. IEEE Trans. Med. Imaging 43(11), 3755\u20133766 (2024). https:\/\/doi.org\/10.1109\/TMI.2024.3398350","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4385_CR75","doi-asserted-by":"publisher","unstructured":"S. Wang, Y. Guo, Y. Wang, H. Sun, and J. Huang, (2019) \u201cSMILES-BERT,\u201d in Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics, New York, pp. 429\u2013436. https:\/\/doi.org\/10.1145\/3307339.3342186.","DOI":"10.1145\/3307339.3342186"},{"key":"4385_CR76","doi-asserted-by":"publisher","unstructured":"R. Singh et al., (2025) ChemBERTa-3: An open source training framework for chemical foundation models. https:\/\/doi.org\/10.26434\/chemrxiv-2025-4glrl.","DOI":"10.26434\/chemrxiv-2025-4glrl"},{"issue":"21","key":"4385_CR77","doi-asserted-by":"publisher","DOI":"10.3390\/ijms252111744","volume":"25","author":"T Song","year":"2024","unstructured":"Song, T., Song, H., Pan, Z., Gao, Y., Dai, H., Wang, X.: Deepdualenhancer: a dual-feature input DNABert based deep learning method for enhancer recognition. Int. J. Mol. Sci. 25(21), 11744 (2024). https:\/\/doi.org\/10.3390\/ijms252111744","journal-title":"Int. J. Mol. Sci."},{"issue":"3","key":"4385_CR78","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1021\/acsomega.1c05203","volume":"7","author":"Y Kalakoti","year":"2022","unstructured":"Kalakoti, Y., Yadav, S., Sundar, D.: TransDTI: transformer-based language models for estimating DTIs and building a drug recommendation workflow. ACS Omega 7(3), 2706\u20132717 (2022). https:\/\/doi.org\/10.1021\/acsomega.1c05203","journal-title":"ACS Omega"},{"key":"4385_CR79","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad226","author":"T ValizadehAslani","year":"2023","unstructured":"ValizadehAslani, T., et al.: PharmBERT: a domain-specific BERT model for drug labels. Brief. Bioinform. (2023). https:\/\/doi.org\/10.1093\/bib\/bbad226","journal-title":"Brief. Bioinform."},{"key":"4385_CR80","unstructured":"E. Nguyen et al., (2023)\u201cHyenaDNA: Long-range genomic sequence modeling at single nucleotide resolution.,\u201d ArXiv"},{"key":"4385_CR81","unstructured":"S. Mo et al., (2021) Multi-modal self-supervised pre-training for regulatory genome across cell types"},{"key":"4385_CR82","doi-asserted-by":"publisher","unstructured":"Y. Chen and J. Zou, (2023) GenePT: a simple but effective foundation model for genes and cells built from ChatGPT. https:\/\/doi.org\/10.1101\/2023.10.16.562533.","DOI":"10.1101\/2023.10.16.562533"},{"issue":"1","key":"4385_CR83","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104297","volume":"63","author":"D Wang","year":"2026","unstructured":"Wang, D., Cheng, T., Wang, S., Chen, Y (Frank)., Yin, Y.: SMR-agents: synergistic medical reasoning agents for zero-shot medical visual question answering with MLLMs. Inf. Process. Manag. 63(1), 104297 (2026). https:\/\/doi.org\/10.1016\/j.ipm.2025.104297","journal-title":"Inf. Process. Manag."},{"issue":"10","key":"4385_CR84","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","volume":"44","author":"A Elnaggar","year":"2022","unstructured":"Elnaggar, A., et al.: ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 7112\u20137127 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3095381","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4385_CR85","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae270","author":"R Mall","year":"2024","unstructured":"Mall, R., Singh, A., Patel, C.N., Guirimand, G., Castiglione, F.: VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction. Brief. Bioinform. (2024). https:\/\/doi.org\/10.1093\/bib\/bbae270","journal-title":"Brief. Bioinform."},{"issue":"8016","key":"4385_CR86","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","volume":"630","author":"J Abramson","year":"2024","unstructured":"Abramson, J., et al.: Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630(8016), 493\u2013500 (2024). https:\/\/doi.org\/10.1038\/s41586-024-07487-w","journal-title":"Nature"},{"issue":"8","key":"4385_CR87","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","volume":"38","author":"N Brandes","year":"2022","unstructured":"Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., Linial, M.: ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38(8), 2102\u20132110 (2022). https:\/\/doi.org\/10.1093\/bioinformatics\/btac020","journal-title":"Bioinformatics"},{"issue":"1","key":"4385_CR88","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1186\/s12911-024-02656-3","volume":"24","author":"T Mirzaei","year":"2024","unstructured":"Mirzaei, T., Amini, L., Esmaeilzadeh, P.: Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications. BMC Med. Inform. Decis. Mak. 24(1), 250 (2024). https:\/\/doi.org\/10.1186\/s12911-024-02656-3","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"4385_CR89","doi-asserted-by":"publisher","DOI":"10.2196\/72062","volume":"27","author":"H Su","year":"2025","unstructured":"Su, H., et al.: Large language models in medical diagnostics: scoping review with bibliometric analysis. J. Med. Internet Res. 27, e72062 (2025). https:\/\/doi.org\/10.2196\/72062","journal-title":"J. Med. Internet Res."},{"issue":"1","key":"4385_CR90","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01024-9","volume":"7","author":"T Li","year":"2024","unstructured":"Li, T., et al.: CancerGPT for few shot drug pair synergy prediction using large pretrained language models. NPJ Digit. Med. 7(1), 40 (2024). https:\/\/doi.org\/10.1038\/s41746-024-01024-9","journal-title":"NPJ Digit. Med."},{"issue":"2","key":"4385_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2025.100153","volume":"3","author":"Z Liu","year":"2025","unstructured":"Liu, Z., et al.: Radiology-GPT: a large language model for radiology. Meta-Radiology 3(2), 100153 (2025). https:\/\/doi.org\/10.1016\/j.metrad.2025.100153","journal-title":"Meta-Radiology"},{"key":"4385_CR92","doi-asserted-by":"publisher","unstructured":"O. C. Thawakar et al., (2024) XrayGPT: chest radiographs summarization using large medical vision-language models, In Stroudsburg, association for computational linguistics, pp. 440\u2013448. https:\/\/doi.org\/10.18653\/v1\/2024.bionlp-1.35.","DOI":"10.18653\/v1\/2024.bionlp-1.35"},{"issue":"5","key":"4385_CR93","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1038\/s41591-024-02959-y","volume":"30","author":"M Christensen","year":"2024","unstructured":"Christensen, M., Vukadinovic, M., Yuan, N., Ouyang, D.: Vision\u2013language foundation model for echocardiogram interpretation. Nat. Med. 30(5), 1481\u20131488 (2024). https:\/\/doi.org\/10.1038\/s41591-024-02959-y","journal-title":"Nat. Med."},{"key":"4385_CR94","unstructured":"J. Chen et al., (2024) HuatuoGPT-II, One-stage training for medical adaption of LLMs"},{"issue":"10","key":"4385_CR95","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s42256-022-00534-z","volume":"4","author":"F Yang","year":"2022","unstructured":"Yang, F., et al.: scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat. Mach. Intell. 4(10), 852\u2013866 (2022). https:\/\/doi.org\/10.1038\/s42256-022-00534-z","journal-title":"Nat. Mach. Intell."},{"issue":"8","key":"4385_CR96","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1038\/s41592-024-02201-0","volume":"21","author":"H Cui","year":"2024","unstructured":"Cui, H., et al.: scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21(8), 1470\u20131480 (2024). https:\/\/doi.org\/10.1038\/s41592-024-02201-0","journal-title":"Nat. Methods"},{"key":"4385_CR97","unstructured":"G. Wang, G. Yang, Z. Du, L. Fan, and X. Li, (2023) ClinicalGPT: large language models finetuned with diverse medical data and comprehensive evaluation"},{"key":"4385_CR98","doi-asserted-by":"publisher","unstructured":"J. Zhu, Q. Gong, C. Zhou, and H. Luan, (2023) \u201cZhongJing: a locally deployed large language model for traditional Chinese medicine and corresponding evaluation methodology: a large language model for data fine-tuning in the field of traditional Chinese medicine, and a new evaluation method called TCMEval are proposed,\u201d In proceedings Of The 2023 4th international symposium on artificial intelligence for medicine science, New York, pp. 1036\u20131042. https:\/\/doi.org\/10.1145\/3644116.3644294.","DOI":"10.1145\/3644116.3644294"},{"issue":"1","key":"4385_CR99","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-52417-z","volume":"15","author":"P Qiu","year":"2024","unstructured":"Qiu, P., et al.: Towards building multilingual language model for medicine. Nat. Commun. 15(1), 8384 (2024). https:\/\/doi.org\/10.1038\/s41467-024-52417-z","journal-title":"Nat. Commun."},{"key":"4385_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.103001","volume":"157","author":"Z Deng","year":"2024","unstructured":"Deng, Z., et al.: OphGLM: an ophthalmology large language-and-vision assistant. Artif. Intell. Med. 157, 103001 (2024). https:\/\/doi.org\/10.1016\/j.artmed.2024.103001","journal-title":"Artif. Intell. Med."},{"key":"4385_CR101","doi-asserted-by":"publisher","DOI":"10.2196\/32690","volume":"8","author":"J Ji","year":"2024","unstructured":"Ji, J., Hou, Y., Chen, X., Pan, Y., Xiang, Y.: Vision-language model for generating textual descriptions from clinical images: model development and validation study. JMIR Form. Res. 8, e32690 (2024). https:\/\/doi.org\/10.2196\/32690","journal-title":"JMIR Form. Res."},{"key":"4385_CR102","unstructured":"K. Saab et al., (2024) Capabilities of gemini models in medicine"},{"issue":"1","key":"4385_CR103","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00958-w","volume":"6","author":"C Peng","year":"2023","unstructured":"Peng, C., et al.: A study of generative large language model for medical research and healthcare. NPJ Digit. Med. 6(1), 210 (2023). https:\/\/doi.org\/10.1038\/s41746-023-00958-w","journal-title":"NPJ Digit. Med."},{"key":"4385_CR104","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad472","author":"J Ma","year":"2023","unstructured":"Ma, J., et al.: \u2018Bingo\u2019\u2014a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data. Brief. Bioinform. (2023). https:\/\/doi.org\/10.1093\/bib\/bbad472","journal-title":"Brief. Bioinform."},{"key":"4385_CR105","doi-asserted-by":"crossref","unstructured":"S. Zhang et al., (2025) \u201cBiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","DOI":"10.1056\/AIoa2400640"},{"issue":"7414","key":"4385_CR106","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/nature11247","volume":"489","author":"ENCODE Project Consortium","year":"2012","unstructured":"ENCODE Project Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414), 57\u201374 (2012). https:\/\/doi.org\/10.1038\/nature11247","journal-title":"Nature"},{"key":"4385_CR107","doi-asserted-by":"crossref","unstructured":"Cancer Genome Atlas Research Network et al. The cancer genome atlas pan-cancer analysis project. Nat Genet. 45, 1113-20 (2013)","DOI":"10.1038\/ng.2764"},{"issue":"D1","key":"4385_CR108","doi-asserted-by":"publisher","first-page":"D941","DOI":"10.1093\/nar\/gky1015","volume":"47","author":"JG Tate","year":"2019","unstructured":"Tate, J.G., et al.: COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47(D1), D941\u2013D947 (2019). https:\/\/doi.org\/10.1093\/nar\/gky1015","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"4385_CR109","doi-asserted-by":"publisher","first-page":"D609","DOI":"10.1093\/nar\/gkae1010","volume":"53","author":"A Bateman","year":"2025","unstructured":"Bateman, A., et al.: UniProt: the universal protein knowledgebase in 2025. Nucleic Acids Res. 53(D1), D609\u2013D617 (2025). https:\/\/doi.org\/10.1093\/nar\/gkae1010","journal-title":"Nucleic Acids Res."},{"issue":"12","key":"4385_CR110","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1101\/gr.096651.109","volume":"19","author":"J Peterson","year":"2009","unstructured":"Peterson, J., et al.: The NIH human microbiome project. Genome Res. 19(12), 2317\u20132323 (2009). https:\/\/doi.org\/10.1101\/gr.096651.109","journal-title":"Genome Res."},{"issue":"1","key":"4385_CR111","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/nar\/29.1.308","volume":"29","author":"ST Sherry","year":"2001","unstructured":"Sherry, S.T.: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29(1), 308\u2013311 (2001). https:\/\/doi.org\/10.1093\/nar\/29.1.308","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"4385_CR112","doi-asserted-by":"publisher","first-page":"D1062","DOI":"10.1093\/nar\/gkx1153","volume":"46","author":"MJ Landrum","year":"2018","unstructured":"Landrum, M.J., et al.: ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46(D1), D1062\u2013D1067 (2018). https:\/\/doi.org\/10.1093\/nar\/gkx1153","journal-title":"Nucleic Acids Res."},{"key":"4385_CR113","doi-asserted-by":"publisher","DOI":"10.1002\/cpz1.226","author":"L Gong","year":"2021","unstructured":"Gong, L., Whirl\u2010Carrillo, M., Klein, T.E.: PharmGKB, an integrated resource of pharmacogenomic knowledge. Curr. Protoc. (2021). https:\/\/doi.org\/10.1002\/cpz1.226","journal-title":"Curr. Protoc."},{"issue":"3","key":"4385_CR114","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C., et al.: UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12(3), e1001779 (2015). https:\/\/doi.org\/10.1371\/journal.pmed.1001779","journal-title":"PLoS Med."},{"issue":"1","key":"4385_CR115","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson, A.E.W., et al.: MIMIC-III, a freely accessible critical care database. Sci Data 3(1), 160035 (2016). https:\/\/doi.org\/10.1038\/sdata.2016.35","journal-title":"Sci Data"},{"key":"4385_CR116","unstructured":"J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, (2019) \u201cBERT: Pre-training of deep bidirectional transformers for language understanding"},{"issue":"3","key":"4385_CR117","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100034","volume":"1","author":"A Nazir","year":"2023","unstructured":"Nazir, A., Cheeema, M.N., Wang, Z.: ChatGPT-based biological and psychological data imputation. Meta-Radiology 1(3), 100034 (2023). https:\/\/doi.org\/10.1016\/j.metrad.2023.100034","journal-title":"Meta-Radiology"},{"key":"4385_CR118","unstructured":"C. Raffel et al., (2023) Exploring the limits of transfer learning with a unified text-to-text transformer"},{"key":"4385_CR119","unstructured":"T. Han et al., (2025) MedAlpaca -an open-source collection of medical conversational ai models and training data"},{"key":"4385_CR120","doi-asserted-by":"crossref","unstructured":"Z. Du et al., (2022) \u201cGLM: general language model pretraining with autoregressive blank infilling","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"4385_CR121","unstructured":"A. Zeng et al., (2023) \u201cGLM-130B: an open bilingual pre-trained model"},{"key":"4385_CR122","unstructured":"J. Kaplan et al., (2020) Scaling laws for neural language models"},{"key":"4385_CR123","unstructured":"J. Hoffmann et al., (2022)Training compute-optimal large language models"},{"key":"4385_CR124","unstructured":"A. Radford et al., (2021) Learning transferable visual models from natural language supervision"},{"key":"4385_CR125","doi-asserted-by":"crossref","unstructured":"J.-B. Alayrac et al., (2022) Flamingo: a visual language model for few-shot learning","DOI":"10.52202\/068431-1723"},{"key":"4385_CR126","unstructured":"J. Li, D. Li, S. Savarese, and S. Hoi, (2023) \u201cBLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models"},{"key":"4385_CR127","doi-asserted-by":"crossref","unstructured":"G. Pa\u0142ka and A. Nowakowski, (2023) Exploring the use of foundation models for named entity recognition and lemmatization tasks in slavic languages","DOI":"10.18653\/v1\/2023.bsnlp-1.19"},{"issue":"1","key":"4385_CR128","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024). https:\/\/doi.org\/10.1038\/s41467-024-44824-z","journal-title":"Nat. Commun."},{"key":"4385_CR129","doi-asserted-by":"crossref","unstructured":"Z. Huang, X. Zhang, and S. Zhang, (2023) KiUT: knowledge-injected u-transformer for radiology report generation","DOI":"10.1109\/CVPR52729.2023.01897"},{"key":"4385_CR130","unstructured":"C. Wu, X. Zhang, Y. Zhang, Y. Wang, and W. Xie, (2023) Towards generalist foundation model for radiology by leveraging Web-scale 2D&3D medical data"},{"key":"4385_CR131","doi-asserted-by":"publisher","first-page":"7897","DOI":"10.1007\/s10489-024-05558-z","volume":"54","author":"MR Pacheco-Lorenzo","year":"2024","unstructured":"Pacheco-Lorenzo, M.R., Anido-Rif\u00f3n, L.E., Fern\u00e1ndez-Iglesias, M.J., Valladares-Rodr\u00edguez, S.M.: Will senior adults accept being cognitively assessed by a conversational agent? A user-interaction pilot study. Appl. Intell. 54, 7897\u20137912 (2024). https:\/\/doi.org\/10.1007\/s10489-024-05558-z","journal-title":"Appl. Intell."},{"key":"4385_CR132","unstructured":"A. Dosovitskiy et al., (2021) An image is worth 16x16 words: transformers for image recognition at scale"},{"key":"4385_CR133","unstructured":"A. Vaid et al., (2024) Natural language programming in medicine: administering evidence based clinical workflows with autonomous agents powered by generative large language models"},{"key":"4385_CR134","doi-asserted-by":"publisher","unstructured":"J. S. Park, J. O\u2019Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, (2023) \u201cGenerative Agents: interactive simulacra of human behavior,\u201d In proceedings of the 36th annual ACM symposium on user interface software and technology, New York, pp. 1\u201322. https:\/\/doi.org\/10.1145\/3586183.3606763.","DOI":"10.1145\/3586183.3606763"},{"key":"4385_CR135","doi-asserted-by":"publisher","unstructured":"B. Boecking et al., (2022) Making the\u00a0most of\u00a0text semantics to\u00a0improve biomedical vision\u2013language processing pp. 1\u201321. https:\/\/doi.org\/10.1007\/978-3-031-20059-5_1.","DOI":"10.1007\/978-3-031-20059-5_1"},{"issue":"14","key":"4385_CR136","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.3390\/app11146421","journal-title":"Appl. Sci."},{"issue":"3","key":"4385_CR137","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","volume":"31","author":"K Singhal","year":"2025","unstructured":"Singhal, K., et al.: Toward expert-level medical question answering with large language models. Nat. Med. 31(3), 943\u2013950 (2025). https:\/\/doi.org\/10.1038\/s41591-024-03423-7","journal-title":"Nat. Med."},{"key":"4385_CR138","doi-asserted-by":"crossref","unstructured":"G. Xiong, Q. Jin, Z. Lu, and A. Zhang, (2024) Benchmarking retrieval-augmented generation for medicine","DOI":"10.18653\/v1\/2024.findings-acl.372"},{"issue":"1","key":"4385_CR139","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-024-11709-7","volume":"57","author":"P Bruno","year":"2025","unstructured":"Bruno, P., Quarta, A., Calimeri, F.: Continual learning in medicine: a systematic literature review. Neural. Process. Lett. 57(1), 2 (2025). https:\/\/doi.org\/10.1007\/s11063-024-11709-7","journal-title":"Neural. Process. Lett."},{"issue":"11","key":"4385_CR140","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000651","volume":"3","author":"JL Cross","year":"2024","unstructured":"Cross, J.L., Choma, M.A., Onofrey, J.A.: Bias in medical AI: implications for clinical decision-making. PLoS Digit. Health 3(11), e0000651 (2024). https:\/\/doi.org\/10.1371\/journal.pdig.0000651","journal-title":"PLoS Digit. Health"},{"key":"4385_CR141","unstructured":"A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell, (2017) \u201cWhat do we need to build explainable AI systems for the medical domain?"},{"key":"4385_CR142","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.971943","author":"B Sheng","year":"2022","unstructured":"Sheng, B., et al.: An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front. Public Health (2022). https:\/\/doi.org\/10.3389\/fpubh.2022.971943","journal-title":"Front. Public Health"},{"issue":"6","key":"4385_CR143","doi-asserted-by":"publisher","first-page":"e428","DOI":"10.1016\/S2589-7500(24)00061-X","volume":"6","author":"JCL Ong","year":"2024","unstructured":"Ong, J.C.L., et al.: Ethical and regulatory challenges of large language models in medicine. Lancet Digit. Health 6(6), e428\u2013e432 (2024). https:\/\/doi.org\/10.1016\/S2589-7500(24)00061-X","journal-title":"Lancet Digit. Health"},{"key":"4385_CR144","doi-asserted-by":"crossref","unstructured":"A. Pal, L. K. Umapathi, and M. Sankarasubbu, (2023) \u201cMed-HALT: medical domain hallucination test for large language models","DOI":"10.18653\/v1\/2023.conll-1.21"},{"issue":"1","key":"4385_CR145","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17(1), 195 (2019). https:\/\/doi.org\/10.1186\/s12916-019-1426-2","journal-title":"BMC Med."},{"issue":"1","key":"4385_CR146","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-07619-7","volume":"9","author":"L Maier-Hein","year":"2018","unstructured":"Maier-Hein, L., et al.: Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9(1), 5217 (2018). https:\/\/doi.org\/10.1038\/s41467-018-07619-7","journal-title":"Nat. Commun."},{"issue":"4","key":"4385_CR147","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1007\/s00371-023-02985-w","volume":"40","author":"S Masood","year":"2024","unstructured":"Masood, S., et al.: Deep choroid layer segmentation using hybrid features extraction from OCT images. Vis. Comput. 40(4), 2775\u20132792 (2024). https:\/\/doi.org\/10.1007\/s00371-023-02985-w","journal-title":"Vis. Comput."},{"issue":"7","key":"4385_CR148","doi-asserted-by":"publisher","first-page":"4893","DOI":"10.1007\/s00371-024-03697-5","volume":"41","author":"S Masood","year":"2025","unstructured":"Masood, S., Al Bashrawi, M.A., Khan, M.A., Nazir, A.: Exploring ChatGPT applications in healthcare: a comprehensive overview. Vis. Comput. 41(7), 4893\u20134914 (2025). https:\/\/doi.org\/10.1007\/s00371-024-03697-5","journal-title":"Vis. Comput."},{"key":"4385_CR149","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2019.04.003","volume":"72","author":"DSW Ting","year":"2019","unstructured":"Ting, D.S.W., et al.: Deep learning in ophthalmology: the technical and clinical considerations. Prog. Retin. Eye Res. 72, 100759 (2019). https:\/\/doi.org\/10.1016\/j.preteyeres.2019.04.003","journal-title":"Prog. Retin. Eye Res."},{"key":"4385_CR150","doi-asserted-by":"crossref","unstructured":"W. Wang et al., (2025) \u201cA survey of LLM-based agents in medicine: how far are we from Baymax?","DOI":"10.18653\/v1\/2025.findings-acl.539"},{"key":"4385_CR151","unstructured":"H. Zhou et al., (2024) A survey of large language models in medicine: progress, application, and challenge"},{"key":"4385_CR152","doi-asserted-by":"publisher","DOI":"10.2196\/58670","author":"Y Gao","year":"2025","unstructured":"Gao, Y., et al.: Leveraging medical knowledge graphs into large language models for diagnosis prediction: design and application study. JMIR AI (2025). https:\/\/doi.org\/10.2196\/58670","journal-title":"JMIR AI"},{"issue":"7969","key":"4385_CR153","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-023-06160-y","volume":"619","author":"LY Jiang","year":"2023","unstructured":"Jiang, L.Y., et al.: Health system-scale language models are all-purpose prediction engines. Nature 619(7969), 357\u2013362 (2023). https:\/\/doi.org\/10.1038\/s41586-023-06160-y","journal-title":"Nature"},{"key":"4385_CR154","doi-asserted-by":"crossref","unstructured":"C.-W. Huang, S.-C. Tsai, and Y.-N. Chen, (2022) PLM-ICD: automatic ICD coding with pretrained language models","DOI":"10.18653\/v1\/2022.clinicalnlp-1.2"},{"key":"4385_CR155","doi-asserted-by":"publisher","unstructured":"J. Liu, S. Yang, T. Peng, X. Hu, and Q. Zhu, (2023) ChatICD: prompt learning for few-shot ICD coding through ChatGPT,\u201d In 2023 IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, pp. 4360\u20134367. https:\/\/doi.org\/10.1109\/BIBM58861.2023.10385482.","DOI":"10.1109\/BIBM58861.2023.10385482"},{"key":"4385_CR156","unstructured":"X. Zhao, T. Wang, and A. Rios, (2024) \u201cImproving expert radiology report summarization by prompting large language models with a layperson summary"},{"key":"4385_CR157","doi-asserted-by":"crossref","unstructured":"S. Wang, Z. Zhao, X. Ouyang, Q. Wang, and D. Shen, (2023) \u201cChatCAD: Interactive computer-aided diagnosis on medical image using large language models","DOI":"10.1038\/s44172-024-00271-8"},{"key":"4385_CR158","doi-asserted-by":"publisher","unstructured":"W. Xie, C. Wu, X. Zhang, Y. Zhang, and Y. Wang, (2023) Towards generalist foundation model for radiology https:\/\/doi.org\/10.21203\/rs.3.rs-3324530\/v1.","DOI":"10.21203\/rs.3.rs-3324530\/v1"},{"key":"4385_CR159","doi-asserted-by":"publisher","unstructured":"V. Rawte et al., The troubling emergence of hallucination in large language models an extensive definition, quantification, and prescriptive remediations,\u201d In proceedings of the 2023 conference on empirical methods in natural language processing, Stroudsburg, Association for computational linguistics, pp. 2541\u20132573. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.155.","DOI":"10.18653\/v1\/2023.emnlp-main.155"},{"issue":"9","key":"4385_CR160","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10849-5","volume":"57","author":"A Moglia","year":"2024","unstructured":"Moglia, A., Georgiou, K., Cerveri, P., Mainardi, L., Satava, R.M., Cuschieri, A.: Large language models in healthcare: from a systematic review on medical examinations to a comparative analysis on fundamentals of robotic surgery online test. Artif. Intell. Rev. 57(9), 231 (2024). https:\/\/doi.org\/10.1007\/s10462-024-10849-5","journal-title":"Artif. Intell. Rev."},{"key":"4385_CR161","doi-asserted-by":"publisher","DOI":"10.2196\/48291","volume":"9","author":"A Abd-alrazaq","year":"2023","unstructured":"Abd-alrazaq, A., et al.: Large language models in medical education: opportunities, challenges, and future directions. JMIR Med. Educ. 9, e48291 (2023). https:\/\/doi.org\/10.2196\/48291","journal-title":"JMIR Med. Educ."},{"key":"4385_CR162","doi-asserted-by":"publisher","unstructured":"W. Dai et al., (2023) \u201cCan large language models provide feedback to students? A case study on ChatGPT,\u201d In 2023 IEEE International conference on advanced learning technologies (ICALT), IEEE, pp. 323\u2013325. https:\/\/doi.org\/10.1109\/ICALT58122.2023.00100.","DOI":"10.1109\/ICALT58122.2023.00100"},{"key":"4385_CR163","unstructured":"L. Yang et al., (2024) Advancing multimodal medical capabilities of Gemini"},{"issue":"1","key":"4385_CR164","doi-asserted-by":"publisher","first-page":"1","DOI":"10.51219\/JAIMLD\/oluwole-fagbohun\/19","volume":"2","author":"O Fagbohun","year":"2024","unstructured":"Fagbohun, O., Iduwe, N.P., Abdullahi, M., Ifaturoti, A., Nwanna, O.M.: Beyond traditional assessment: exploring the impact of large language models on grading practices. J. Artif. Intell. Mach. Learn. Data Sci. 2(1), 1\u20138 (2024). https:\/\/doi.org\/10.51219\/JAIMLD\/oluwole-fagbohun\/19","journal-title":"J. Artif. Intell. Mach. Learn. Data Sci."},{"key":"4385_CR165","doi-asserted-by":"publisher","unstructured":"Z. Guo, A. Lai, J. H. Thygesen, J. Farrington, T. Keen, and K. Li, (2024) \u201cLarge language model for mental health: a systematic review, https:\/\/doi.org\/10.2196\/preprints.57400.","DOI":"10.2196\/preprints.57400"},{"key":"4385_CR166","doi-asserted-by":"publisher","unstructured":"H. Qiu, A. Li, L. Ma, and Z. Lan, (2024) \u201cPsyChat: A client-centric dialogue system for mental health support,\u201d In 2024 27th international conference on computer supported cooperative work in design (CSCWD), IEEE, pp. 2979\u20132984. https:\/\/doi.org\/10.1109\/CSCWD61410.2024.10580641.","DOI":"10.1109\/CSCWD61410.2024.10580641"},{"key":"4385_CR167","unstructured":"J. M. Liu, D. Li, H. Cao, T. Ren, Z. Liao, and J. Wu, (2023)\u201cChatCounselor: a large language models for mental health support"},{"key":"4385_CR168","doi-asserted-by":"publisher","first-page":"e59479","DOI":"10.2196\/59479","volume":"11","author":"HR Lawrence","year":"2024","unstructured":"Lawrence, H.R., Schneider, R.A., Rubin, S.B., Matari\u0107, M.J., McDuff, D.J., Jones Bell, M.: The opportunities and risks of large language models in mental health. JMIR Ment. Health 11, e59479\u2013e59479 (2024). https:\/\/doi.org\/10.2196\/59479","journal-title":"JMIR Ment. Health"},{"key":"4385_CR169","doi-asserted-by":"publisher","unstructured":"K. Yang, T. Zhang, Z. Kuang, Q. Xie, J. Huang, and S. Ananiadou, (2024) \u201cMentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models,\u201d In proceedings of the ACM web conference 2024, New York, pp. 4489\u20134500. https:\/\/doi.org\/10.1145\/3589334.3648137.","DOI":"10.1145\/3589334.3648137"},{"key":"4385_CR170","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ajem.2024.12.024","volume":"89","author":"B Arslan","year":"2025","unstructured":"Arslan, B., Nuhoglu, C., Satici, M.O., Altinbilek, E.: Evaluating LLM-based generative AI tools in emergency triage: a comparative study of ChatGPT Plus, Copilot Pro, and triage nurses. Am. J. Emerg. Med. 89, 174\u2013181 (2025). https:\/\/doi.org\/10.1016\/j.ajem.2024.12.024","journal-title":"Am. J. Emerg. Med."},{"issue":"5","key":"4385_CR171","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1111\/all.15976","volume":"79","author":"L Shu","year":"2024","unstructured":"Shu, L., et al.: Human\u2010in\u2010the\u2010loop: human involvement in enhancing medical inquiry performance in large language models. Allergy 79(5), 1348\u20131351 (2024). https:\/\/doi.org\/10.1111\/all.15976","journal-title":"Allergy"},{"key":"4385_CR172","doi-asserted-by":"publisher","DOI":"10.2196\/51834","volume":"3","author":"M Quttainah","year":"2024","unstructured":"Quttainah, M., Mishra, V., Madakam, S., Lurie, Y., Mark, S.: Cost, usability, credibility, fairness, accountability, transparency, and explainability framework for safe and effective large language models in medical education: narrative review and qualitative study. JMIR AI 3, e51834 (2024). https:\/\/doi.org\/10.2196\/51834","journal-title":"JMIR AI"},{"key":"4385_CR173","unstructured":"Q. Wu et al., (2023) AutoGen: Enabling next-gen LLM applications via multi-agent conversation"},{"issue":"1","key":"4385_CR174","doi-asserted-by":"publisher","DOI":"10.1007\/s11701-024-01867-0","volume":"18","author":"JE Knudsen","year":"2024","unstructured":"Knudsen, J.E., Ghaffar, U., Ma, R., Hung, A.J.: Clinical applications of artificial intelligence in robotic surgery. J. Robot. Surg. 18(1), 102 (2024). https:\/\/doi.org\/10.1007\/s11701-024-01867-0","journal-title":"J. Robot. Surg."},{"key":"4385_CR175","doi-asserted-by":"crossref","unstructured":"Q. Jin, B. Dhingra, Z. Liu, W. W. Cohen, and X. Lu, (2019) \u201cPubMedQA: a dataset for biomedical research question answering,\u201d","DOI":"10.18653\/v1\/D19-1259"},{"key":"4385_CR176","doi-asserted-by":"publisher","unstructured":"S. Lin, J. Hilton, and O. Evans, (2022) TruthfulQA: measuring how models mimic human falsehoods, In Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers), Stroudsburg,: association for computational linguistics, pp. 3214\u20133252. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.229.","DOI":"10.18653\/v1\/2022.acl-long.229"},{"key":"4385_CR177","doi-asserted-by":"crossref","unstructured":"V. Rawte et al., (2023) \u201cThe troubling emergence of hallucination in large language models an extensive definition, quantification, and prescriptive remediations","DOI":"10.18653\/v1\/2023.emnlp-main.155"},{"issue":"11","key":"4385_CR178","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000651","volume":"3","author":"JL Cross","year":"2024","unstructured":"Cross, J.L., Choma, M.A., Onofrey, J.A.: Bias in medical AI: implications for clinical decision-making. PLOS Digital Health 3(11), e0000651 (2024). https:\/\/doi.org\/10.1371\/journal.pdig.0000651","journal-title":"PLOS Digital Health"},{"issue":"3","key":"4385_CR179","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlpt.2024.100892","volume":"13","author":"YSJ Aquino","year":"2024","unstructured":"Aquino, Y.S.J., et al.: Defining change: exploring expert views about the regulatory challenges in adaptive artificial intelligence for healthcare. Health Policy Technol. 13(3), 100892 (2024). https:\/\/doi.org\/10.1016\/j.hlpt.2024.100892","journal-title":"Health Policy Technol."},{"key":"4385_CR180","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-024-10508-8","author":"Y Li","year":"2024","unstructured":"Li, Y., Goel, S.: Making it possible for the auditing of AI: a systematic review of AI audits and AI auditability. Inf. Syst. Front. (2024). https:\/\/doi.org\/10.1007\/s10796-024-10508-8","journal-title":"Inf. Syst. Front."},{"issue":"12","key":"4385_CR181","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1038\/s44222-024-00245-7","volume":"2","author":"B van Breugel","year":"2024","unstructured":"van Breugel, B., Liu, T., Oglic, D., van der Schaar, M.: Synthetic data in biomedicine via generative artificial intelligence. Nat. Rev. Bioeng. 2(12), 991\u20131004 (2024). https:\/\/doi.org\/10.1038\/s44222-024-00245-7","journal-title":"Nat. Rev. Bioeng."},{"issue":"3","key":"4385_CR182","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2022","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69(3), 1173\u20131185 (2022). https:\/\/doi.org\/10.1109\/TBME.2021.3117407","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"4385_CR183","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101535","volume":"58","author":"A Chartsias","year":"2019","unstructured":"Chartsias, A., et al.: Disentangled representation learning in cardiac image analysis. Med. Image Anal. 58, 101535 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.101535","journal-title":"Med. Image Anal."},{"issue":"1","key":"4385_CR184","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller, M.J., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10(1), 12598 (2020). https:\/\/doi.org\/10.1038\/s41598-020-69250-1","journal-title":"Sci. Rep."},{"issue":"10","key":"4385_CR185","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","volume":"27","author":"I Dayan","year":"2021","unstructured":"Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with COVID-19. Nat. Med. 27(10), 1735\u20131743 (2021). https:\/\/doi.org\/10.1038\/s41591-021-01506-3","journal-title":"Nat. Med."},{"key":"4385_CR186","unstructured":"X. Li, M. Jiang, X. Zhang, M. Kamp, and Q. Dou, (2021)\u201cFedBN: federated learning on non-IID features via local batch normalization"},{"issue":"6","key":"4385_CR187","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1038\/s42256-021-00337-8","volume":"3","author":"G Kaissis","year":"2021","unstructured":"Kaissis, G., et al.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6), 473\u2013484 (2021). https:\/\/doi.org\/10.1038\/s42256-021-00337-8","journal-title":"Nat. Mach. Intell."},{"issue":"19","key":"4385_CR188","doi-asserted-by":"publisher","first-page":"30065","DOI":"10.1007\/s11042-022-14267-z","volume":"82","author":"SQ Gilani","year":"2023","unstructured":"Gilani, S.Q., Marques, O.: Skin lesion analysis using generative adversarial networks: a review. Multimed. Tools Appl. 82(19), 30065\u201330106 (2023). https:\/\/doi.org\/10.1007\/s11042-022-14267-z","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"4385_CR189","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00699-2","volume":"5","author":"H Chen","year":"2022","unstructured":"Chen, H., Gomez, C., Huang, C.-M., Unberath, M.: Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review. NPJ Digit. Med. 5(1), 156 (2022). https:\/\/doi.org\/10.1038\/s41746-022-00699-2","journal-title":"NPJ Digit. Med."},{"key":"4385_CR190","doi-asserted-by":"publisher","unstructured":"L. Dixon, J. Li, J. Sorensen, N. Thain, and L. Vasserman, (2018) Measuring and mitigating unintended bias in text classification, In proceedings of the 2018 AAAI\/ACM conference on AI, ethics, and society, New York, pp. 67\u201373. https:\/\/doi.org\/10.1145\/3278721.3278729.","DOI":"10.1145\/3278721.3278729"},{"key":"4385_CR191","doi-asserted-by":"publisher","unstructured":"B. H. Zhang, B. Lemoine, and M. Mitchell, (2018) Mitigating unwanted biases with adversarial learning, In Proceedings of the 2018 AAAI\/ACM Conference on AI, ethics, and society, New York, pp. 335\u2013340. https:\/\/doi.org\/10.1145\/3278721.3278779.","DOI":"10.1145\/3278721.3278779"},{"key":"4385_CR192","doi-asserted-by":"crossref","unstructured":"S. Rui et al., (2025) Multi-modal vision pre-training for medical image analysis","DOI":"10.1109\/CVPR52734.2025.00487"},{"key":"4385_CR193","doi-asserted-by":"crossref","unstructured":"L. Seyyed-Kalantari, G. Liu, M. McDermott, I. Y. Chen, and M. Ghassemi, (2020) CheXclusion: fairness gaps in deep chest X-ray classifiers","DOI":"10.1142\/9789811232701_0022"},{"key":"4385_CR194","doi-asserted-by":"crossref","unstructured":"M. Groh et al., (2021) Evaluating deep neural networks trained on clinical images in dermatology with the Fitzpatrick 17k dataset","DOI":"10.1109\/CVPRW53098.2021.00201"},{"issue":"13","key":"4385_CR195","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1001\/jama.2024.13486","volume":"332","author":"RM Ratwani","year":"2024","unstructured":"Ratwani, R.M., Sutton, K., Galarraga, J.E.: Addressing AI algorithmic bias in health care. JAMA 332(13), 1051 (2024). https:\/\/doi.org\/10.1001\/jama.2024.13486","journal-title":"JAMA"},{"key":"4385_CR196","doi-asserted-by":"publisher","unstructured":"M. Mitchell et al., (2019) Model cards for model reporting,\u201d In proceedings of the conference on fairness, accountability, and transparency, New York, pp. 220\u2013229. https:\/\/doi.org\/10.1145\/3287560.3287596.","DOI":"10.1145\/3287560.3287596"},{"issue":"12","key":"4385_CR197","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1145\/3458723","volume":"64","author":"T Gebru","year":"2021","unstructured":"Gebru, T., et al.: Datasheets for datasets. Commun. ACM 64(12), 86\u201392 (2021). https:\/\/doi.org\/10.1145\/3458723","journal-title":"Commun. ACM"},{"key":"4385_CR198","doi-asserted-by":"publisher","unstructured":"A. Said, A. Yahyaoui, and T. Abdellatif, (2024) HIPAA and\u00a0GDPR compliance in\u00a0IoT healthcare systems, pp. 198\u2013209. https:\/\/doi.org\/10.1007\/978-3-031-55729-3_16.","DOI":"10.1007\/978-3-031-55729-3_16"},{"key":"4385_CR199","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107818","volume":"99","author":"FA KhoKhar","year":"2022","unstructured":"KhoKhar, F.A., Shah, J.H., Khan, M.A., Sharif, M., Tariq, U., Kadry, S.: A review on federated learning towards image processing. Comput. Electr. Eng. 99, 107818 (2022). https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107818","journal-title":"Comput. Electr. Eng."},{"key":"4385_CR200","doi-asserted-by":"crossref","unstructured":"T. Wu, L. Luo, Y.-F. Li, S. Pan, T.-T. Vu, and G. Haffari, (2024) Continual learning for large language models: a survey","DOI":"10.18653\/v1\/2025.emnlp-tutorials.7"},{"key":"4385_CR201","unstructured":"H. Shi et al., (2024) Continual learning of large language models: a comprehensive survey"},{"issue":"8","key":"4385_CR202","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3716629","volume":"57","author":"J Zheng","year":"2025","unstructured":"Zheng, J., Qiu, S., Shi, C., Ma, Q.: Towards lifelong learning of large language models: a survey. ACM Comput. Surv. 57(8), 1\u201335 (2025). https:\/\/doi.org\/10.1145\/3716629","journal-title":"ACM Comput. Surv."},{"key":"4385_CR203","unstructured":"Q. Zheng, Z. Xu, A. Choudhry, Y. Chen, Y. Li, and Y. Huang, (2023) \u201cSynergizing human-AI agency: a guide of 23 heuristics for service co-creation with LLM-based agents"},{"key":"4385_CR204","doi-asserted-by":"publisher","unstructured":"G. Perkovi\u0107, A. Drobnjak, and I. Boti\u010dki, (2024) \u201cHallucinations in LLMs: understanding and addressing challenges,\u201d In 2024 47th MIPRO ICT and electronics convention (MIPRO), IEEE, pp. 2084\u20132088. https:\/\/doi.org\/10.1109\/MIPRO60963.2024.10569238.","DOI":"10.1109\/MIPRO60963.2024.10569238"},{"issue":"7972","key":"4385_CR205","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). https:\/\/doi.org\/10.1038\/s41586-023-06291-2","journal-title":"Nature"},{"key":"4385_CR206","doi-asserted-by":"crossref","unstructured":"S. Lin, J. Hilton, and O. Evans, (2022) TruthfulQA: measuring how models mimic human falsehoods","DOI":"10.18653\/v1\/2022.acl-long.229"},{"key":"4385_CR207","doi-asserted-by":"crossref","unstructured":"J. Li, X. Cheng, W. X. Zhao, J.-Y. Nie, and J.-R. Wen, (2023) HaluEval: A large-scale hallucination evaluation benchmark for large language models","DOI":"10.18653\/v1\/2023.emnlp-main.397"},{"key":"4385_CR208","doi-asserted-by":"crossref","unstructured":"Z. Song et al., (2025) Injecting domain-specific knowledge into large language models: a comprehensive survey","DOI":"10.18653\/v1\/2025.findings-emnlp.1379"},{"issue":"2","key":"4385_CR209","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3709365","volume":"6","author":"S Neupane","year":"2025","unstructured":"Neupane, S., et al.: Medinsight: a multi-source context augmentation framework for generating patient-centric medical responses using large language models. ACM Trans. Comput. Healthc. 6(2), 1\u201319 (2025). https:\/\/doi.org\/10.1145\/3709365","journal-title":"ACM Trans. Comput. Healthc."},{"key":"4385_CR210","doi-asserted-by":"publisher","unstructured":"N. Lukas, A. Salem, R. Sim, S. Tople, L. Wutschitz, and S. Zanella-B\u00e9guelin, (2023) Analyzing Leakage of Personally Identifiable Information in Language Models, In 2023 IEEE symposium on security and privacy (SP), IEEE, pp. 346\u2013363. https:\/\/doi.org\/10.1109\/SP46215.2023.10179300.","DOI":"10.1109\/SP46215.2023.10179300"},{"issue":"4","key":"4385_CR211","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1001\/jama.2023.9651","volume":"330","author":"T Minssen","year":"2023","unstructured":"Minssen, T., Vayena, E., Cohen, I.G.: The challenges for regulating medical use of ChatGPT and other large language models. JAMA 330(4), 315 (2023). https:\/\/doi.org\/10.1001\/jama.2023.9651","journal-title":"JAMA"},{"key":"4385_CR212","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.40895","author":"Y Li","year":"2023","unstructured":"Li, Y., Li, Z., Zhang, K., Dan, R., Jiang, S., Zhang, Y.: ChatDoctor: a medical chat model fine-tuned on a large language model meta-AI (LLaMA) using medical domain knowledge. Cureus (2023). https:\/\/doi.org\/10.7759\/cureus.40895","journal-title":"Cureus"},{"key":"4385_CR213","doi-asserted-by":"crossref","unstructured":"Y. Labrak, A. Bazoge, E. Morin, P.-A. Gourraud, M. Rouvier, and R. Dufour, (2024) BioMistral: a collection of open-source pretrained large language models for medical domains","DOI":"10.18653\/v1\/2024.findings-acl.348"},{"key":"4385_CR214","unstructured":"S. Yang et al., (2025) Exploring large language models in healthcare: insights into corpora sources, customization strategies, and evaluation metrics"},{"key":"4385_CR215","unstructured":"Z. Zhu, Y. Yang, and Z. Sun, (2024) HaluEval-wild: evaluating hallucinations of language models in the wild"},{"key":"4385_CR216","doi-asserted-by":"publisher","DOI":"10.1021\/acsami.4c00648","author":"T Hu","year":"2024","unstructured":"Hu, T., Sheng, B.: A Highly sensitive strain sensor with wide linear sensing range prepared on a hybrid-structured CNT\/Ecoflex film via local regulation of strain distribution. ACS Appl. Mater. Interfaces (2024). https:\/\/doi.org\/10.1021\/acsami.4c00648","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"4385_CR217","doi-asserted-by":"publisher","unstructured":"M. Bollaert, O. Augereau, and G. Coppin, (2024)Measuring and\u00a0calibrating trust in\u00a0artificial intelligence, pp. 232\u2013237. https:\/\/doi.org\/10.1007\/978-3-031-61698-3_22.","DOI":"10.1007\/978-3-031-61698-3_22"},{"key":"4385_CR218","doi-asserted-by":"crossref","unstructured":"S. Bannur et al., (2023) Learning to exploit temporal structure for biomedical vision-language processing","DOI":"10.1109\/CVPR52729.2023.01442"},{"key":"4385_CR219","doi-asserted-by":"crossref","unstructured":"L. Li, L. Dinh, S. Hu, and L. Hemphill, (2024) \u201cAcademic collaboration on large language model studies increases overall but varies across disciplines","DOI":"10.21203\/rs.3.rs-4887385\/v1"},{"key":"4385_CR220","doi-asserted-by":"publisher","unstructured":"D. Raj, S. SAHU, and A. Anand, (2017) Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text, In Proceedings of the 21st conference on computational natural language\u00a0learning (CoNLL 2017), Stroudsburg: Association for computational linguistics, pp. 311\u2013321. https:\/\/doi.org\/10.18653\/v1\/K17-1032.","DOI":"10.18653\/v1\/K17-1032"},{"issue":"9","key":"4385_CR221","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.1038\/s41588-023-01465-0","volume":"55","author":"N Brandes","year":"2023","unstructured":"Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., Ntranos, V.: Genome-wide prediction of disease variant effects with a deep protein language model. Nat. Genet. 55(9), 1512\u20131522 (2023). https:\/\/doi.org\/10.1038\/s41588-023-01465-0","journal-title":"Nat. Genet."},{"key":"4385_CR222","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad651","author":"Q Jin","year":"2023","unstructured":"Jin, Q., et al.: MedCPT: contrastive pre-trained transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval. Bioinformatics (2023). https:\/\/doi.org\/10.1093\/bioinformatics\/btad651","journal-title":"Bioinformatics"},{"issue":"4","key":"4385_CR223","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1038\/s41591-024-02887-x","volume":"30","author":"C Kim","year":"2024","unstructured":"Kim, C., et al.: Transparent medical image AI via an image\u2013text foundation model grounded in medical literature. Nat. Med. 30(4), 1154\u20131165 (2024). https:\/\/doi.org\/10.1038\/s41591-024-02887-x","journal-title":"Nat. Med."},{"issue":"1","key":"4385_CR224","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson, A.E.W., et al.: MIMIC-IV, a freely accessible electronic health record dataset. Sci Data 10(1), 1 (2023). https:\/\/doi.org\/10.1038\/s41597-022-01899-x","journal-title":"Sci Data"},{"key":"4385_CR225","unstructured":"M. Hu, S. Pan, Y. Li, and X. Yang, (2023) Advancing medical imaging with language models: a journey from N-grams to ChatGPT"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04385-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04385-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04385-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:22:17Z","timestamp":1774455737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04385-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":225,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["4385"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04385-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,21]]},"assertion":[{"value":"13 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"176"}}