A S Vickram, Shofia Saghya Infant, Priyanka, Hitesh Chopra
{"title":"AI-Powered Techniques in Anatomical Imaging: Impacts on Veterinary Diagnostics and Surgery.","authors":"A S Vickram, Shofia Saghya Infant, Priyanka, Hitesh Chopra","doi":"10.1016/j.aanat.2024.152355","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutional neural networks, show promise in interpreting medical images from various modalities like X-rays, ultrasounds, CT scans, and MRI/mammography.</p><p><strong>Study design: </strong>Narrative review OBJECTIVE: This review aims to explore the innovations and challenges of AI-enabled imaging tools in veterinary diagnostics and surgery, highlighting their potential impact on diagnostic accuracy, surgical risk mitigation, and personalized veterinary healthcare.</p><p><strong>Methods: </strong>We reviewed recent literature on AI applications in veterinary diagnostic imaging, focusing on their benefits, limitations, and future directions.</p><p><strong>Conclusion: </strong>AI-enabled imaging tools hold immense potential for revolutionizing veterinary diagnostics and surgery. By enhancing diagnostic accuracy, enabling precise surgical planning, and supporting personalized treatment strategies, AI can significantly improve animal health outcomes. However, addressing challenges related to data privacy, algorithm bias, and integration into clinical workflows is crucial for the widespread adoption and success of these transformative technologies.</p>","PeriodicalId":93872,"journal":{"name":"Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft","volume":" ","pages":"152355"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.aanat.2024.152355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutional neural networks, show promise in interpreting medical images from various modalities like X-rays, ultrasounds, CT scans, and MRI/mammography.
Study design: Narrative review OBJECTIVE: This review aims to explore the innovations and challenges of AI-enabled imaging tools in veterinary diagnostics and surgery, highlighting their potential impact on diagnostic accuracy, surgical risk mitigation, and personalized veterinary healthcare.
Methods: We reviewed recent literature on AI applications in veterinary diagnostic imaging, focusing on their benefits, limitations, and future directions.
Conclusion: AI-enabled imaging tools hold immense potential for revolutionizing veterinary diagnostics and surgery. By enhancing diagnostic accuracy, enabling precise surgical planning, and supporting personalized treatment strategies, AI can significantly improve animal health outcomes. However, addressing challenges related to data privacy, algorithm bias, and integration into clinical workflows is crucial for the widespread adoption and success of these transformative technologies.
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