{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T17:06:42Z","timestamp":1771348002640,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Melanoma is one of the top 5 cancer types, causes most deaths among skin cancers, and can be frequently misdiagnosed. Recent pathology image foundation models remain difficult to make accurate differential diagnosis across over forty melanocytic neoplasm histologic subtypes. Motivated by the diagnostic reasoning process of dermatopathologists, we curated a high-quality image and knowledge corpus database containing 2893 images and 1102 knowledge entries annotated by expert dermatopathologists at the University of Pennsylvania. Leveraging this multi-modal dataset, we present \u201cMelan-Dx\u201d, a knowledge-enhanced AI framework that augments frozen pathology vision-language models through retrieval from a curated vision-knowledge database, improving differential diagnosis at both patch and whole-slide levels. Melan-Dx, at its best performance, demonstrates 0.869 accuracy for binary classification, 0.699 Top-1 accuracy among forty-class classification, 0.915 ROC AUC for few-shot WSI tasks, and 0.925 AUPRC for fully supervised WSI tasks. Across all experimental settings, Melan-Dx shows improvements up to 13.8% over linear and fully finetuned methods, 23\u201370.6% over zero-shot approaches and up to 8.4% improvements in whole slide image classification. These findings suggest that a query database with a knowledge-enhanced AI framework can further improve existing pathology foundation models without fine-tuning the vision backbone. The code is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/www.github.com\/zhihuanglab\/Melan-Dx-code\" ext-link-type=\"uri\">https:\/\/www.github.com\/zhihuanglab\/Melan-Dx-code<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1038\/s41746-026-02357-3","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T10:35:48Z","timestamp":1768905348000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Melan-Dx: a knowledge-enhanced vision-language framework improves differential diagnosis of melanocytic neoplasm pathology"],"prefix":"10.1038","volume":"9","author":[{"given":"Jialu","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peixian","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Elder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"2357_CR1","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.mpmed.2009.02.016","volume":"37","author":"S Ogden","year":"2009","unstructured":"Ogden, S. & Telfer, N. R. Skin cancer. Medicine 37, 305\u2013308 (2009).","journal-title":"Medicine"},{"key":"2357_CR2","unstructured":"American Cancer Society. Cancer facts and figures 2024 https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/2024-cancer-facts-figures.html (2024)."},{"key":"2357_CR3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.14740\/wjon1349","volume":"12","author":"PP Naik","year":"2021","unstructured":"Naik, P. P. Cutaneous malignant melanoma: a review of early diagnosis and management. World J. Oncol. 12, 7 (2021).","journal-title":"World J. Oncol."},{"key":"2357_CR4","first-page":"906","volume":"15","author":"J Liersch","year":"2017","unstructured":"Liersch, J., von K\u00f6ckritz, A. & Schaller, J. Dermatopathology 101. Part 2\u2013skin tumors. J. Dtsch. Dermatol. Ges. 15, 906\u2013929 (2017).","journal-title":"J. Dtsch. Dermatol. Ges."},{"key":"2357_CR5","doi-asserted-by":"crossref","first-page":"ii21","DOI":"10.1136\/bmjqs-2012-001615","volume":"22","author":"ML Graber","year":"2013","unstructured":"Graber, M. L. The incidence of diagnostic error in medicine. BMJ Qual. Saf. 22, ii21\u2013ii27 (2013).","journal-title":"BMJ Qual. Saf."},{"key":"2357_CR6","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1136\/bmjebm-2023-112460","volume":"29","author":"AS Adamson","year":"2024","unstructured":"Adamson, A. S., Naik, G., Jones, M. A. & Bell, K. J. Ecological study estimating melanoma overdiagnosis in the USA using the lifetime risk method. BMJ Evid. Based Med. 29, 156\u2013161 (2024).","journal-title":"BMJ Evid. Based Med."},{"key":"2357_CR7","doi-asserted-by":"crossref","first-page":"e4332","DOI":"10.1097\/MD.0000000000004332","volume":"95","author":"W Sondermann","year":"2016","unstructured":"Sondermann, W. et al. Initial misdiagnosis of melanoma located on the foot is associated with poorer prognosis. Medicine 95, e4332 (2016).","journal-title":"Medicine"},{"key":"2357_CR8","first-page":"1236","volume":"18","author":"H Kutzner","year":"2020","unstructured":"Kutzner, H. et al. Overdiagnosis of melanoma\u2013causes, consequences and solutions. J. Dtsch. Dermatol. Ges. 18, 1236\u20131243 (2020).","journal-title":"J. Dtsch. Dermatol. Ges."},{"key":"2357_CR9","volume":"7","author":"KP Venkatesh","year":"2024","unstructured":"Venkatesh, K. P., Raza, M. M., Nickel, G., Wang, S. & Kvedar, J. C. Deep learning models across the range of skin disease. NPJ Digit. Med. 7, 32 (2024).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR10","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41746-023-00900-0","volume":"6","author":"K Venkatesh","year":"2023","unstructured":"Venkatesh, K., Raza, M. & Kvedar, J. AI-based skin cancer detection: the balance between access and overutilization. NPJ Digit. Med 6, 147 (2023).","journal-title":"NPJ Digit. Med"},{"key":"2357_CR11","volume":"5","author":"JS Marwaha","year":"2022","unstructured":"Marwaha, J. S. & Kvedar, J. C. Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI. NPJ Digit. Med. 5, 25 (2022).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR12","volume":"6","author":"M Mittermaier","year":"2023","unstructured":"Mittermaier, M., Raza, M. M. & Kvedar, J. C. Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digit. Med. 6, 113 (2023).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR13","volume":"6","author":"M Mittermaier","year":"2023","unstructured":"Mittermaier, M., Raza, M. & Kvedar, J. C. Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches. NPJ Digit. Med. 6, 137 (2023).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR14","volume":"5","author":"KP Venkatesh","year":"2022","unstructured":"Venkatesh, K. P., Raza, M. M., Diao, J. A. & Kvedar, J. C. Leveraging reimbursement strategies to guide value-based adoption and utilization of medical AI. NPJ Digit. Med. 5, 112 (2022).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR15","volume":"7","author":"MM Raza","year":"2024","unstructured":"Raza, M. M., Venkatesh, K. P. & Kvedar, J. C. Generative ai and large language models in health care: pathways to implementation. NPJ Digit. Med. 7, 62 (2024).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR16","volume":"7","author":"JC Kwong","year":"2024","unstructured":"Kwong, J. C., Nickel, G. C., Wang, S. C. & Kvedar, J. C. Integrating artificial intelligence into healthcare systems: more than just the algorithm. NPJ Digit. Med. 7, 52 (2024).","journal-title":"NPJ Digit. Med."},{"key":"2357_CR17","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301\u20131309 (2019).","journal-title":"Nat. Med."},{"key":"2357_CR18","doi-asserted-by":"crossref","first-page":"e91","DOI":"10.1158\/0008-5472.CAN-17-0313","volume":"77","author":"J Cheng","year":"2017","unstructured":"Cheng, J. et al. Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Res. 77, e91\u2013e100 (2017).","journal-title":"Cancer Res."},{"key":"2357_CR19","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1016\/j.ajpath.2012.01.040","volume":"180","author":"LA Cooper","year":"2012","unstructured":"Cooper, L. A. et al. The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma. Am. J. Pathol. 180, 2108\u20132119 (2012).","journal-title":"Am. J. Pathol."},{"key":"2357_CR20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.celrep.2018.03.086","volume":"23","author":"J Saltz","year":"2018","unstructured":"Saltz, J. et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 23, 181\u2013193 (2018).","journal-title":"Cell Rep."},{"key":"2357_CR21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.4103\/jpi.jpi_31_18","volume":"9","author":"TG Olsen","year":"2018","unstructured":"Olsen, T. G. et al. Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology. J. Pathol. Inform. 9, 32 (2018).","journal-title":"J. Pathol. Inform."},{"key":"2357_CR22","doi-asserted-by":"crossref","first-page":"107083","DOI":"10.1016\/j.compbiomed.2023.107083","volume":"163","author":"D Sauter","year":"2023","unstructured":"Sauter, D. et al. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review. Comput. Biol. Med. 163, 107083 (2023).","journal-title":"Comput. Biol. Med."},{"key":"2357_CR23","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ejca.2019.06.012","volume":"118","author":"A Hekler","year":"2019","unstructured":"Hekler, A. et al. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur. J. Cancer 118, 91\u201396 (2019).","journal-title":"Eur. J. Cancer"},{"key":"2357_CR24","unstructured":"Ghezloo, F. et al. Pathfinder: A multi-modal multi-agent system for medical diagnostic decision-making applied to histopathology. In Proc. IEEE\/CVF International Conference on Computer Vision (ICCV), 23431\u201323441 (2025)."},{"key":"2357_CR25","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s44385-025-00013-1","volume":"2","author":"RT Lucassen","year":"2025","unstructured":"Lucassen, R. T., Stathonikos, N., Breimer, G. E., Veta, M. & Blokx, W. A. Artificial intelligence-based triaging of cutaneous melanocytic lesions. NPJ Biomed. Innov. 2, 10 (2025).","journal-title":"NPJ Biomed. Innov."},{"key":"2357_CR26","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1111\/bjd.20656","volume":"186","author":"J White","year":"2022","unstructured":"White, J., Lui, H., Chute, C. G., Jakob, R. & Chalmers, R. J. The WHO ICD-11 classification of dermatological diseases: a new comprehensive online skin disease taxonomy designed by and for dermatologists. Br. J. Dermatol. 186, 178\u2013179 (2022).","journal-title":"Br. J. Dermatol."},{"key":"2357_CR27","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1007\/s11760-022-02325-w","volume":"17","author":"I Bakkouri","year":"2023","unstructured":"Bakkouri, I. & Afdel, K. Mlca2f: Multi-level context attentional feature fusion for COVID-19 lesion segmentation from CT scans. Signal Image Video Process. 17, 1181\u20131188 (2023).","journal-title":"Signal Image Video Process."},{"key":"2357_CR28","doi-asserted-by":"crossref","first-page":"10743","DOI":"10.1007\/s11042-022-12242-2","volume":"81","author":"I Bakkouri","year":"2022","unstructured":"Bakkouri, I., Afdel, K., Benois-Pineau, J. & Initiative, G. C. F.tA. D. N. Bg-3dm2f: bidirectional gated 3d multi-scale feature fusion for alzheimer\u2019s disease diagnosis. Multimed. Tools Appl. 81, 10743\u201310776 (2022).","journal-title":"Multimed. Tools Appl."},{"key":"2357_CR29","doi-asserted-by":"crossref","first-page":"20483","DOI":"10.1007\/s11042-019-07988-1","volume":"79","author":"I Bakkouri","year":"2020","unstructured":"Bakkouri, I. & Afdel, K. Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed. Tools Appl. 79, 20483\u201320518 (2020).","journal-title":"Multimed. Tools Appl."},{"key":"2357_CR30","doi-asserted-by":"crossref","first-page":"12939","DOI":"10.1007\/s11042-018-6267-z","volume":"78","author":"I Bakkouri","year":"2019","unstructured":"Bakkouri, I. & Afdel, K. Multi-scale CNN based on region proposals for efficient breast abnormality recognition. Multimed. Tools Appl. 78, 12939\u201312960 (2019).","journal-title":"Multimed. Tools Appl."},{"key":"2357_CR31","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","volume":"30","author":"RJ Chen","year":"2024","unstructured":"Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850\u2013862 (2024).","journal-title":"Nat. Med."},{"key":"2357_CR32","doi-asserted-by":"crossref","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 from real-world data. Nature 630, 181\u2013188 (2024).","journal-title":"Nature"},{"key":"2357_CR33","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1038\/s41591-025-03747-y","volume":"31","author":"S Yan","year":"2025","unstructured":"Yan, S. et al. A multimodal vision foundation model for clinical dermatology. Nat. Medicine 31, 2691\u20132702 (2025).","journal-title":"Nat. Medicine"},{"key":"2357_CR34","doi-asserted-by":"crossref","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\u2013language foundation model for pathology image analysis using medical twitter. Nat. Med. 29, 2307\u20132316 (2023).","journal-title":"Nat. Med."},{"key":"2357_CR35","doi-asserted-by":"crossref","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":"2357_CR36","unstructured":"Sun, Y. et al. Pathgen-1.6m: 1.6 million pathology image-text pairs generation through multi-agent collaboration. In International Conference on Representation Learning, (eds Yue, Y., Garg, A., Peng, N., Sha, F. & Yu, R.) Vol. 2025, 94611\u201394653 (2025)."},{"key":"2357_CR37","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1038\/s41586-024-08378-w","volume":"638","author":"J Xiang","year":"2025","unstructured":"Xiang, J. et al. A vision-language foundation model for precision oncology. Nature 638, 769\u2013778 (2025).","journal-title":"Nature"},{"key":"2357_CR38","doi-asserted-by":"publisher","unstructured":"Codella, N. et al. Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.1902.03368 (2019).","DOI":"10.48550\/arXiv.1902.03368"},{"key":"2357_CR39","doi-asserted-by":"crossref","first-page":"106221","DOI":"10.1016\/j.dib.2020.106221","volume":"32","author":"AG Pacheco","year":"2020","unstructured":"Pacheco, A. G. et al. Pad-ufes-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Br. 32, 106221 (2020).","journal-title":"Data Br."},{"key":"2357_CR40","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer Analysis Project. Nat. Genet. 45, 1113\u20131120 (2013).","journal-title":"Nat. Genet."},{"key":"2357_CR41","first-page":"37995","volume":"36","author":"W Ikezogwo","year":"2023","unstructured":"Ikezogwo, W. et al. Quilt-1m: one million image-text pairs for histopathology. Adv. Neural Inf. Process. Syst. 36, 37995\u201338017 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2357_CR42","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.jaad.2013.07.027","volume":"70","author":"MW Piepkorn","year":"2014","unstructured":"Piepkorn, M. W. et al. The mpath-dx reporting schema for melanocytic proliferations and melanoma. J. Am. Acad. Dermatol. 70, 131\u2013141 (2014).","journal-title":"J. Am. Acad. Dermatol."},{"key":"2357_CR43","unstructured":"Nechaev, D., Pchelnikov, A. & Ivanova, E. Histai: an open-source, large-scale whole slide image dataset for computational pathology. https:\/\/arxiv.org\/abs\/2505.12120 (2025)."},{"key":"2357_CR44","unstructured":"Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In Proc. International conference on machine learning, 2127\u20132136 (PMLR, 2018)."},{"key":"2357_CR45","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.1038\/s41591-025-03982-3","volume":"31","author":"T Ding","year":"2025","unstructured":"Ding, T. et al. A multimodal whole-slide foundation model for pathology. Nat. Med. 31, 3749\u20133761 (2025).","journal-title":"Nat. Med."},{"key":"2357_CR46","unstructured":"Bioptimus. H-optimus-1 https:\/\/huggingface.co\/bioptimus\/H-optimus-1 (2025)."},{"key":"2357_CR47","doi-asserted-by":"publisher","unstructured":"Zimmermann, E. et al. Virchow2: scaling self-supervised mixed magnification models in pathology. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.2408.00738 (2024).","DOI":"10.48550\/arXiv.2408.00738"},{"key":"2357_CR48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3458754","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu, Y. et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. 3, 1\u201323 (2021).","journal-title":"ACM Trans. Comput. Healthc."},{"key":"2357_CR49","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/s41551-024-01223-5","volume":"9","author":"Z Huang","year":"2025","unstructured":"Huang, Z. et al. A pathologist\u2013ai collaboration framework for enhancing diagnostic accuracies and efficiencies. Nat. Biomed. Eng. 9, 455\u2013470 (2025).","journal-title":"Nat. Biomed. Eng."},{"key":"2357_CR50","unstructured":"Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems, (eds Guyon, I. et al.) Vol. 30 (2017)."},{"key":"2357_CR51","unstructured":"Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In International Conference on Learning Representations (2019)."},{"key":"2357_CR52","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E. J. et al. Lora: Low-rank adaptation of large language models. ICLR 1, 3 (2022).","journal-title":"ICLR"},{"key":"2357_CR53","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-023-02585-2","volume":"10","author":"A Mosquera-Zamudio","year":"2023","unstructured":"Mosquera-Zamudio, A. et al. A spitzoid tumor dataset with clinical metadata and whole slide images for deep learning models. Sci. Data 10, 704 (2023).","journal-title":"Sci. Data"},{"key":"2357_CR54","unstructured":"Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. https:\/\/arxiv.org\/abs\/1412.6980 (2017)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02357-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02357-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02357-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T16:09:12Z","timestamp":1771344552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02357-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2357"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02357-3","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,20]]},"assertion":[{"value":"20 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"171"}}