{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:29:51Z","timestamp":1777487391566,"version":"3.51.4"},"reference-count":8,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis. Comput. Ind. Biomed. Art"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Efficient and accurate assignment of journal submissions to suitable associate editors (AEs) is critical in maintaining review quality and timeliness, particularly in high-volume, rapidly evolving fields such as medical imaging. This study investigates the feasibility of leveraging large language models for AE-paper matching in IEEE Transactions on Medical Imaging. An AE database was curated from historical AE assignments and AE-authored publications, and extracted six key textual components from each paper title, four categories of structured keywords, and abstracts. ModernBERT was employed locally to generate high-dimensional semantic embeddings, which were then reduced using principal component analysis (PCA) for efficient similarity computation. Keyword similarity, derived from structured domain-specific metadata, and textual similarity from ModernBERT embeddings were combined to rank the candidate AEs. Experiments on internal (historical assignments) and external (AE Publications) test sets showed that keyword similarity is the dominant contributor to matching performance. Contrarily, textual similarity offers complementary gains, particularly when PCA is applied. Ablation studies confirmed that structured keywords alone provide strong matching accuracy, with titles offering additional benefits and abstracts offering minimal improvements. The proposed approach offers a practical, interpretable, and scalable tool for editorial workflows, reduces manual workload, and supports high-quality peer reviews.<\/jats:p>","DOI":"10.1186\/s42492-025-00212-y","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T02:03:22Z","timestamp":1768183402000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence-aided assignment of journal submissions to associate editors\u2014a feasibility study on IEEE transactions on medical imaging"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6045-8457","authenticated-orcid":false,"given":"Xuanang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gloria","family":"Nwachukwu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongming","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uwe","family":"Kruger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"212_CR1","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, ACL, Minneapolis, 2\u20137 June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"212_CR2","unstructured":"OpenAI (2023) Gpt-4 technical report. arXiv preprint arXiv: 2303.08774"},{"key":"212_CR3","unstructured":"Liang WX, Izzo Z, Zhang YH, Lepp H, Cao HC, Zhao XD et al (2024) Monitoring AI-modified content at scale: a case study on the impact of ChatGPT on AI conference peer reviews. In: Proceedings of the 41st international conference on machine learning, OpenReview.net, Vienna, 21\u201327 July 2024"},{"key":"212_CR4","doi-asserted-by":"publisher","unstructured":"Warner B, Chaffin A, Clavi\u00e9 B, Weller O, Hallstr\u00f6m O, Taghadouini S et al (2025) Smarter, better, faster, longer: a modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference. In: Proceedings of the 63rd annual meeting of the association for computational linguistics, ACL, Vienna, 27 July\u20131 August 2025. https:\/\/doi.org\/10.18653\/v1\/2025.acl-long.127","DOI":"10.18653\/v1\/2025.acl-long.127"},{"key":"212_CR5","doi-asserted-by":"publisher","unstructured":"Wang G (2025) Flagship toward the future. IEEE Trans Med Imaging 44(3): 1113\u20131114. https:\/\/doi.org\/10.1109\/TMI.2025.3543049","DOI":"10.1109\/TMI.2025.3543049"},{"key":"212_CR6","unstructured":"Liu YH, Ott M, Goyal N, Du JF, Joshi M, Chen DQ et al (2019) RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv: 1907.11692"},{"key":"212_CR7","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86): 2579\u20132605"},{"key":"212_CR8","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv: 1802.03426","DOI":"10.21105\/joss.00861"}],"container-title":["Visual Computing for Industry, Biomedicine, and Art"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-025-00212-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42492-025-00212-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-025-00212-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T02:03:24Z","timestamp":1768183404000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s42492-025-00212-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":8,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["212"],"URL":"https:\/\/doi.org\/10.1186\/s42492-025-00212-y","relation":{},"ISSN":["2524-4442"],"issn-type":[{"value":"2524-4442","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]},"assertion":[{"value":"25 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}