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Automated analysis of these high-resolution scans faces significant challenges, including data imbalance, information loss, model interpretability and explainability, and scalable Open-Set Recognition (OSR). This paper provides an in-depth algorithm-level review of AI methodologies for herbarium image classification, tracing the development from classical classification models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to cutting-edge multimodal frameworks. In addition to classification, the review further investigates vision-based analytical tasks critical to herbarium image analysis, including specimen image segmentation, label text identification using Large Language Models (LLMs), and Human-in-the-Loop (HITL) quality assurance strategies. Furthermore, this review reveals practical challenges in specimen image analysis along with their promising solutions and potential future directions.<\/jats:p>","DOI":"10.1007\/s10462-025-11408-2","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T06:59:39Z","timestamp":1761893979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A review of artificial intelligence in herbarium specimen image analysis"],"prefix":"10.1007","volume":"58","author":[{"given":"Yu-Yue","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gemma L. 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