Dawen Wu, Xi Huang, Liang Chen, Peixian Hou, Longqian Liu, Guoyuan Yang
{"title":"Integrating artificial intelligence in strabismus management: current research landscape and future directions.","authors":"Dawen Wu, Xi Huang, Liang Chen, Peixian Hou, Longqian Liu, Guoyuan Yang","doi":"10.3389/ebm.2024.10320","DOIUrl":null,"url":null,"abstract":"<p><p>Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these advancements, challenges such as the underrepresentation of diverse strabismus types and reliance on single-source data remain prevalent. Emphasizing the need for inclusive AI systems, future research should focus on expanding AI capabilities with large model technologies, integrating multimodal data to bridge existing gaps, and developing integrated management platforms to better accommodate diverse patient demographics and clinical scenarios.</p>","PeriodicalId":12163,"journal":{"name":"Experimental Biology and Medicine","volume":"249 ","pages":"10320"},"PeriodicalIF":2.8000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11625544/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Experimental Biology and Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/ebm.2024.10320","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these advancements, challenges such as the underrepresentation of diverse strabismus types and reliance on single-source data remain prevalent. Emphasizing the need for inclusive AI systems, future research should focus on expanding AI capabilities with large model technologies, integrating multimodal data to bridge existing gaps, and developing integrated management platforms to better accommodate diverse patient demographics and clinical scenarios.
期刊介绍:
Experimental Biology and Medicine (EBM) is a global, peer-reviewed journal dedicated to the publication of multidisciplinary and interdisciplinary research in the biomedical sciences. EBM provides both research and review articles as well as meeting symposia and brief communications. Articles in EBM represent cutting edge research at the overlapping junctions of the biological, physical and engineering sciences that impact upon the health and welfare of the world''s population.
Topics covered in EBM include: Anatomy/Pathology; Biochemistry and Molecular Biology; Bioimaging; Biomedical Engineering; Bionanoscience; Cell and Developmental Biology; Endocrinology and Nutrition; Environmental Health/Biomarkers/Precision Medicine; Genomics, Proteomics, and Bioinformatics; Immunology/Microbiology/Virology; Mechanisms of Aging; Neuroscience; Pharmacology and Toxicology; Physiology; Stem Cell Biology; Structural Biology; Systems Biology and Microphysiological Systems; and Translational Research.