{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:46:14Z","timestamp":1779896774239,"version":"3.53.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Australian National Health and Medical Research Council","award":["GNT1192469"],"award-info":[{"award-number":["GNT1192469"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02095-y","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T17:18:54Z","timestamp":1764004734000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multimodal analysis of whole slide images in colorectal cancer"],"prefix":"10.1038","volume":"8","author":[{"given":"Jitendra","family":"Jonnagaddala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miljana","family":"Shulajkovska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton","family":"Gradi\u0161ek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toni Rose","family":"Jue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamil Mahmoud El","family":"Chayeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jana","family":"Lipkova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob Nikolas","family":"Kather","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzhou","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"2095_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229\u2013263 (2024).","journal-title":"CA Cancer J. Clin."},{"key":"2095_CR2","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555\u2013570 (2021).","journal-title":"Nat. Biomed. Eng."},{"key":"2095_CR3","unstructured":"Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018), (eds. Dy, J. & Krause, A.), Vol. 80, 2127\u20132136 (PMLR, 2018)."},{"key":"2095_CR4","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/cjp2.312","volume":"9","author":"B Guo","year":"2023","unstructured":"Guo, B. et al. Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: achieving state-of-the-art predictive performance with fewer data using Swin Transformer. J. Pathol. Clin. Res. 9, 223\u2013235 (2023).","journal-title":"J. Pathol. Clin. Res."},{"key":"2095_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-023-00451-3","volume":"7","author":"J Hohn","year":"2023","unstructured":"Hohn, J. et al. Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning. NPJ Precis. Oncol. 7, 98 (2023).","journal-title":"NPJ Precis. Oncol."},{"key":"2095_CR6","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1016\/j.ccell.2023.08.002","volume":"41","author":"SJ Wagner","year":"2023","unstructured":"Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 41, 1650\u20131661.e1654 (2023).","journal-title":"Cancer Cell"},{"key":"2095_CR7","doi-asserted-by":"publisher","first-page":"e33","DOI":"10.1016\/S2589-7500(23)00208-X","volume":"6","author":"X Jiang","year":"2024","unstructured":"Jiang, X. et al. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. Lancet Digit Health 6, e33\u2013e43 (2024).","journal-title":"Lancet Digit Health"},{"key":"2095_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-024-01796-9","volume":"17","author":"M Unger","year":"2024","unstructured":"Unger, M. & Kather, J. N. A systematic analysis of deep learning in genomics and histopathology for precision oncology. BMC Med Genomics 17, 48 (2024).","journal-title":"BMC Med Genomics"},{"key":"2095_CR9","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","volume":"40","author":"J Lipkova","year":"2022","unstructured":"Lipkova, J. et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40, 1095\u20131110 (2022).","journal-title":"Cancer Cell"},{"key":"2095_CR10","doi-asserted-by":"publisher","first-page":"5108","DOI":"10.1093\/bioinformatics\/btac641","volume":"38","author":"K Huang","year":"2022","unstructured":"Huang, K. et al. Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning. Bioinformatics 38, 5108\u20135115 (2022).","journal-title":"Bioinformatics"},{"key":"2095_CR11","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1353446","volume":"14","author":"Y Xu","year":"2024","unstructured":"Xu, Y. et al. Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning. Front. Oncol. 14, 1353446 (2024).","journal-title":"Front. Oncol."},{"key":"2095_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103303","volume":"97","author":"N Marini","year":"2024","unstructured":"Marini, N. et al. Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning. Med Image Anal. 97, 103303 (2024).","journal-title":"Med Image Anal."},{"key":"2095_CR13","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.6947","volume":"13","author":"Y Tan","year":"2024","unstructured":"Tan, Y., Liu, R., Xue, J. W. & Feng, Z. Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables. Cancer Med. 13, e6947 (2024).","journal-title":"Cancer Med."},{"key":"2095_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.925079","volume":"12","author":"W Qiu","year":"2022","unstructured":"Qiu, W. et al. Evaluating the microsatellite instability of colorectal cancer based on multimodal deep learning integrating histopathological and molecular data. Front Oncol. 12, 925079 (2022).","journal-title":"Front Oncol."},{"key":"2095_CR15","doi-asserted-by":"crossref","unstructured":"Lv, Z., et al. PG-TFNet: Transformer-based fusion network integrating pathological images and genomic data for cancer survival analysis. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021) (eds. Huang, Y., Kurgan, L. A., Luo, F., Hu, X.) (IEEE, 2021).","DOI":"10.1109\/BIBM52615.2021.9669445"},{"key":"2095_CR16","doi-asserted-by":"crossref","unstructured":"Lv, Z., Lin, Y., Yan, R., Wang, Y. & Zhang, F. TransSurv: transformer-based survival analysis model integrating histopathological images and genomic data for colorectal cancer. IEEE\/ACM Trans. Comput. Biol. Bioinform. 20 (2023).","DOI":"10.1109\/TCBB.2022.3199244"},{"key":"2095_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104726","volume":"94","author":"J Zhou","year":"2023","unstructured":"Zhou, J. et al. Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality. EBioMedicine 94, 104726 (2023).","journal-title":"EBioMedicine"},{"key":"2095_CR18","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1245\/s10434-020-08659-4","volume":"27","author":"L Shao","year":"2020","unstructured":"Shao, L. et al. Multiparametric MRI and whole slide image-based pretreatment prediction of pathological response to neoadjuvant chemoradiotherapy in rectal cancer: a multicenter radiopathomic study. Ann. Surg. Oncol. 27, 4296\u20134306 (2020).","journal-title":"Ann. Surg. Oncol."},{"key":"2095_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2023.124727","volume":"263","author":"Z Li","year":"2023","unstructured":"Li, Z., Sun, Y., An, F., Chen, H. & Liao, J. Self-supervised clustering analysis of colorectal cancer biomarkers based on multi-scale whole slides image and mass spectrometry imaging fused images. Talanta 263, 124727 (2023).","journal-title":"Talanta"},{"key":"2095_CR20","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","volume":"30","author":"MY Lu","year":"2024","unstructured":"Lu, M. Y. et al. A visual-language foundation model for computational pathology. Nat. Med. 30, 863\u2013874 (2024).","journal-title":"Nat. Med."},{"key":"2095_CR21","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","volume":"40","author":"RJ Chen","year":"2022","unstructured":"Chen, R. J. et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40, 865\u2013878.e866 (2022).","journal-title":"Cancer Cell"},{"key":"2095_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.636451","volume":"11","author":"H Li","year":"2021","unstructured":"Li, H. et al. Integrative analysis of histopathological images and genomic data in colon adenocarcinoma. Front. Oncol. 11, 636451 (2021).","journal-title":"Front. Oncol."},{"key":"2095_CR23","doi-asserted-by":"publisher","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","volume":"35","author":"A Cheerla","year":"2019","unstructured":"Cheerla, A. & Gevaert, O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35, i446\u2013i454 (2019).","journal-title":"Bioinformatics"},{"key":"2095_CR24","doi-asserted-by":"crossref","unstructured":"Jaume, G., et al. Modeling dense multimodal interactions between biological pathways and histology for survival prediction. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), Farhadi, A., Crandall, D., Sato, I., Wu, J., Pless, R. & Akata, Z. (eds) (IEEE Computer Society \/ Computer Vision Foundation, 2024).","DOI":"10.1109\/CVPR52733.2024.01100"},{"key":"2095_CR25","doi-asserted-by":"publisher","unstructured":"Ding, K., Zhou, M., Metaxas, D. N. & Zhang, S. Pathology-and-genomics multimodal transformer for survival outcome prediction. In Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, Greenspan, H., M\u00fcller, H., van Ginneken, B., Madabhushi, A., Martel, A., Maier, A., Stoyanov, D., Baltrescu, A. F., Arbelaez, P. & Goyal, M. (eds), Lecture Notes in Computer Science, vol. 14225 (Springer, Cham, 2023). https:\/\/doi.org\/10.1007\/978-3-031-43987-2_60","DOI":"10.1007\/978-3-031-43987-2_60"},{"key":"2095_CR26","doi-asserted-by":"publisher","DOI":"10.34133\/2022\/9860179","volume":"2022","author":"H Liu","year":"2022","unstructured":"Liu, H. et al. Preoperative prediction of lymph node metastasis in colorectal cancer with deep learning. BME Front. 2022, 9860179 (2022).","journal-title":"BME Front."},{"key":"2095_CR27","doi-asserted-by":"publisher","unstructured":"Liu, Q. et al. (2023). M2 Fusion: Bayesian-Based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction. In: Woo, J., et al. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023 Workshops. MICCAI 2023, Woo, J., Engelhardt, S., de Bruijne, M., Schmidt, C., Sch\u00f6nlieb, C.-B., Maier-Hein, L. & Suk, H.-I. (eds), Lecture Notes in Computer Science, vol 14394. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-47425-5_12","DOI":"10.1007\/978-3-031-47425-5_12"},{"key":"2095_CR28","doi-asserted-by":"publisher","first-page":"e0305268","DOI":"10.1371\/journal.pone.0305268","volume":"19","author":"O Ogundipe","year":"2024","unstructured":"Ogundipe, O., Kurt, Z. & Woo, W. L. Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer. PLoS ONE 19, e0305268 (2024).","journal-title":"PLoS ONE"},{"key":"2095_CR29","doi-asserted-by":"publisher","unstructured":"Lv, Z. et al. A disentangled representation-based multimodal fusion framework integrating pathomics and radiomics for KRAS mutation detection in colorectal cancer. Big Data Mining and Analytics https:\/\/doi.org\/10.26599\/BDMA.2024.9020012 (2024).","DOI":"10.26599\/BDMA.2024.9020012"},{"key":"2095_CR30","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13045-022-01225-3","volume":"15","author":"R Wang","year":"2022","unstructured":"Wang, R. et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J. Hematol. Oncol. 15, 11 (2022).","journal-title":"J. Hematol. Oncol."},{"key":"2095_CR31","doi-asserted-by":"publisher","first-page":"e8","DOI":"10.1016\/S2589-7500(21)00215-6","volume":"4","author":"L Feng","year":"2022","unstructured":"Feng, L. et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 4, e8\u2013e17 (2022).","journal-title":"Lancet Digit Health"},{"key":"2095_CR32","first-page":"23","volume":"7","author":"N Farahani","year":"2015","unstructured":"Farahani, N., Parwani, A. V. & Pantanowitz, L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Lab. Med. Int. 7, 23\u201333 (2015).","journal-title":"Pathol. Lab. Med. Int."},{"key":"2095_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-99554-9","volume":"11","author":"J Jonnagaddala","year":"2021","unstructured":"Jonnagaddala, J., Chen, A., Batongbacal, S. & Nekkantti, C. The OpenDeID corpus for patient de-identification. Sci. Rep. 11, 19973 (2021).","journal-title":"Sci. Rep."},{"key":"2095_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01921-7","volume":"8","author":"H-J Dai","year":"2025","unstructured":"Dai, H.-J. et al. Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records. npj Digital Med. 8, 517 (2025).","journal-title":"npj Digital Med."},{"key":"2095_CR35","first-page":"A68","volume":"19","author":"K Tomczak","year":"2015","unstructured":"Tomczak, K., Czerwinska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. (Pozn.) 19, A68\u2013A77 (2015).","journal-title":"Contemp. Oncol. (Pozn.)"},{"key":"2095_CR36","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1038\/s41586-024-07618-3","volume":"634","author":"MY Lu","year":"2024","unstructured":"Lu, M. Y. et al. A multimodal generative AI copilot for human pathology. Nature 634, 466\u2013473 (2024).","journal-title":"Nature"},{"key":"2095_CR37","doi-asserted-by":"crossref","unstructured":"Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, 2000).","DOI":"10.1007\/978-1-4757-3294-8"},{"key":"2095_CR38","doi-asserted-by":"publisher","unstructured":"Gao, Y., Beijbom, O., Zhang, N. & Darrell, T. Compact bilinear pooling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Arsalan, A. A., Brown, M. S. & Hua, G. (eds), 317\u2013326 (IEEE Computer Society, Las Vegas, NV, USA, 2016). https:\/\/doi.org\/10.1109\/CVPR.2016.41.","DOI":"10.1109\/CVPR.2016.41"},{"key":"2095_CR39","first-page":"387","volume":"225","author":"J Jonnagaddala","year":"2016","unstructured":"Jonnagaddala, J. et al. Integration and analysis of heterogeneous colorectal cancer data for translational research. Stud. Health Technol. Inf. 225, 387\u2013391 (2016).","journal-title":"Stud. Health Technol. Inf."},{"key":"2095_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01207-6","volume":"24","author":"Y Peng","year":"2024","unstructured":"Peng, Y. & Deng, H. Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer. BMC Med. Imaging 24, 24 (2024).","journal-title":"BMC Med. Imaging"},{"key":"2095_CR41","doi-asserted-by":"publisher","first-page":"e0120823","DOI":"10.1371\/journal.pone.0120823","volume":"10","author":"L Heijmen","year":"2015","unstructured":"Heijmen, L. et al. Multimodality imaging to predict response to systemic treatment in patients with advanced colorectal cancer. PLoS ONE 10, e0120823 (2015).","journal-title":"PLoS ONE"},{"key":"2095_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-88831-2","volume":"11","author":"Y Guo","year":"2021","unstructured":"Guo, Y. et al. Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer. Sci. Rep. 11, 9429 (2021).","journal-title":"Sci. Rep."},{"key":"2095_CR43","doi-asserted-by":"crossref","unstructured":"Quero, G. et al. Artificial intelligence in colorectal cancer surgery: present and future perspectives. Cancers (Basel) 14, 3803 (2022).","DOI":"10.3390\/cancers14153803"},{"key":"2095_CR44","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1016\/j.annonc.2023.10.001","volume":"34","author":"L Castelo-Branco","year":"2023","unstructured":"Castelo-Branco, L. et al. ESMO Guidance for Reporting Oncology real-World evidence (GROW). Ann. Oncol. 34, 1097\u20131112 (2023).","journal-title":"Ann. Oncol."},{"key":"2095_CR45","unstructured":"Li, S. & Tang, H. Multimodal alignment and fusion: a survey. Preprint at https:\/\/arxiv.org\/abs\/2411.17040 (2024)."},{"key":"2095_CR46","doi-asserted-by":"crossref","unstructured":"Shao, Z. et al. Generalizability of self-supervised training models for digital pathology: a multicountry comparison in colorectal cancer. JCO Clin. Cancer Inform 7, e2200178 (2023).","DOI":"10.1200\/CCI.22.00178"},{"key":"2095_CR47","doi-asserted-by":"publisher","unstructured":"Neidlinger, P. et al. Benchmarking foundation models as feature extractors for weakly supervised computational pathology. Nat. Biomed. Eng (2025). https:\/\/doi.org\/10.1038\/s41551-025-01516-3","DOI":"10.1038\/s41551-025-01516-3"},{"key":"2095_CR48","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1038\/s41591-023-02504-3","volume":"29","author":"Z Huang","year":"2023","unstructured":"Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual-language foundation model for pathology image analysis using medical Twitter. Nat. Med. 29, 2307\u20132316 (2023).","journal-title":"Nat. Med."},{"key":"2095_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, S. et al. A multimodal biomedical foundation model trained from fifteen million image\u2013text pairs. NEJM AI 2 AIoa2400640 (2025).","DOI":"10.1056\/AIoa2400640"},{"key":"2095_CR50","unstructured":"Radford, A., et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning (ICML 2021), Meila, M. & Zhang, T. (eds), 8748\u20138763 (PMLR, 2021)."},{"key":"2095_CR51","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","volume":"630","author":"H Xu","year":"2024","unstructured":"Xu, H. et al. A whole-slide foundation model for digital pathology using real-world data. Nature 630, 181\u2013188 (2024).","journal-title":"Nature"},{"key":"2095_CR52","doi-asserted-by":"crossref","unstructured":"Xiang, J. et al. A vision-language foundation model for precision oncology. Nature 638, 769\u2013778 (2024).","DOI":"10.1038\/s41586-024-08378-w"},{"key":"2095_CR53","volume":"0","author":"X Wang","year":"2025","unstructured":"Wang, X. et al. Foundation model for predicting prognosis and adjuvant therapy benefit from digital pathology in GI cancers. J. Clin. Oncol. 0, JCO-24-01501 (2025).","journal-title":"J. Clin. Oncol."},{"key":"2095_CR54","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01429-0","volume":"8","author":"J Jonnagaddala","year":"2025","unstructured":"Jonnagaddala, J. & Wong, Z. S.-Y. Privacy preserving strategies for electronic health records in the era of large language models. npj Digital Med. 8, 34 (2025).","journal-title":"npj Digital Med."},{"key":"2095_CR55","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01721-z","volume":"8","author":"M Tran","year":"2025","unstructured":"Tran, M. et al. Situating governance and regulatory concerns for generative artificial intelligence and large language models in medical education. npj Digital Med. 8, 315 (2025).","journal-title":"npj Digital Med."},{"key":"2095_CR56","doi-asserted-by":"publisher","unstructured":"Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 https:\/\/doi.org\/10.1136\/bmj.n71 (2021).","DOI":"10.1136\/bmj.n71"},{"key":"2095_CR57","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/s10654-010-9491-z","volume":"25","author":"A Stang","year":"2010","unstructured":"Stang, A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta analyses. Eur. J. Epidemiol. 25, 603\u2013605 (2010).","journal-title":"Eur. J. Epidemiol."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02095-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02095-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02095-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T18:17:39Z","timestamp":1764181059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02095-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2095"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02095-y","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,24]]},"assertion":[{"value":"31 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"J.J. and J.N.K. serve on the\n                      NPJ Precision Oncology\n                      editorial board. J.N.K declares ongoing consulting services for AstraZeneca, Panakeia, and Bioptimus. Furthermore, he holds shares in StratifAI, Synagen, and Spira Labs, has received an institutional research grant from GSK and AstraZeneca, as well as honoraria from AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, Merck, MSD, BMS, Roche, Pfizer, and Fresenius. No competing financial interests are declared by any of the remaining authors.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"719"}}