[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126948-en":3,"doc-seo-126948-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126948,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Multimodal Machine Learning in Image-Based and Clinical Biomedicine - Survey and Prospects","Machine learning (ML) for medical artificial intelligence has shifted from traditional statistical approaches toward deep learning. This survey maps the current landscape of multimodal ML and its impact on medical image analysis and clinical decision support, highlighting representation, fusion, translation, alignment, and co-learning. The work stresses the need for principled evaluation and practical deployment of multimodal models, considering interactions between decision support systems and healthcare providers. It also notes persistent barriers, including dataset bias and limited “big data” in many biomedical areas.","UCLA  \nUCLA Previously Published Works  \nTitle  \nMultimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects.  \nPermalink  \n[https://escholarship.org/uc/item/7rd9j05c](https://escholarship.org/uc/item/7rd9j05c)  \nJournal  \nInternational Journal of Computer Vision, 132(9)  \nISSN  \n0920-5691  \nAuthors  \nWarner, Elisa  \nLee, Joonsang Hsu, William et al.  \nPublication Date  \n2024  \nDOI  \n10.1007/s11263-024-02032-8  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMultimodal Machine Learning in Image-Based and Clinical  \nBiomedicine: Survey and Prospects  \nElisa Warner1 · Joonsang Lee1 · William Hsu2 · Tanveer Syeda-Mahmood3 · Charles E. Kahn Jr.4 · Olivier Gevaert5 · Arvind Rao1  \nReceived: 30 January 2023 / Accepted: 9 February 2024 / Published online: 23 April 2024 © The Author(s) 2024  \nAbstract  \nMachine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessmentsand practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of “big data” in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.  \nKeywords Machine learning · Multimodal · Representation · Fusion · Translation · Alignment · Co-learning · Artificial intelligence · Data integration  \n\n| Communicated by Paolo Rota. |\n| --- |\n| Dr. Rao as primary corresponding author and Elisa Warner as secondary corresponding author. |\n\nB Elisa Warner [elisawa@umich.edu](elisawa@umich.edu)  \nB Arvind Rao [ukarvind@med.umich.edu](ukarvind@med.umich.edu)  \nJoonsang Lee  \n[leejoons@umich.edu](leejoons@umich.edu)  \nWilliam Hsu  \n[whsu@mednet.ucla.edu](whsu@mednet.ucla.edu)  \nTanveer Syeda-Mahmood  \n[stf@us.ibm.com](stf@us.ibm.com)  \nCharles E. Kahn Jr.  \n[ckahn@upenn.edu](ckahn@upenn.edu)  \nOlivier Gevaert  \n[ogevaert@stanford.edu](ogevaert@stanford.edu)  \n1 Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, 100 Washtenaw Ave., Ann Arbor, MI 48109, USA  \n1 Introduction  \nMachine learning(ML), the process ofleveraging algorithmsand optimization to infer strategies for solving learning tasks, has enabled some of the greatest developments in artificial intelligence (AI) in the last decade, enabling the automated segmentation or class identification of images, the ability to answer nearly any text-based question, and the ability to generate images never seen before. In biomedical research, many of these ML models are quickly being applied to medical images and decision support systems in conjunction with a significant shift from traditional and statistical methods to increasing application of deep learning models. At the same  \n2 Department of Medical and Imaging Informatics, University of California Los Angeles, 924 Westwood Blvd Ste 420, Los Angeles, CA 90024, USA  \n3 Almaden Research Center, IBM, 650 Harry Rd., San Jose, CA 95120, USA  \n4 Department of Radiology, University of Pennsylvania, 3400 Spruce St., Philadelphia, PA 19104, USA  \n5 Center for Biomedical Informatics Research, Stanford, 1265 Welch Road, Stanford, CA 94305, USA  \ntime, the importance of both plentiful and well-curated dat","cbCaicTfQ6cOhH2x","https://ap.wps.com/l/cbCaicTfQ6cOhH2x","pdf",2519640,1,18,"English","en",105,"# Introduction\n## Multimodal Data in Biomedical Research and Clinical Care\n## Applications of Deep Learning in Medical AI\n## Challenges and the Need for Principled Evaluation\n# Survey Focus and Prospects\n## Multimodal Representation and Fusion\n## Translation, Alignment, and Co-learning\n## Integration into Biomedical Practice","[{\"question\":\"What areas does the survey focus on within multimodal machine learning for biomedicine?\",\"answer\":\"It focuses on multimodal representation, fusion, translation, alignment, and co-learning, and how these approaches support clinical predictions and medical image analysis.\"},{\"question\":\"Why is multimodal data especially important in clinical decision models?\",\"answer\":\"Biomedical research and clinical care naturally involve multiple data types, such as imaging sequences plus labs, demographics, ECG, and genetic expression, which reflect different representation spaces.\"},{\"question\":\"What challenges remain despite advances in multimodal models?\",\"answer\":\"The survey highlights data biases and the scarcity of large, well-curated datasets in many biomedical domains, which limits performance and reliability.\"}]","Multimodal Machine Learning in Image-Based and Clinical Biomedicine - Survey and Prospects | PDF",1785935842,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multimodal-machine-learning-in-image-based-and-clinical-biomedicine-survey-and-prospects","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multimodal-machine-learning-in-image-based-and-clinical-biomedicine-survey-and-prospects/126948/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What areas does the survey focus on within multimodal machine learning for biomedicine?","Question",{"text":75,"@type":76},"It focuses on multimodal representation, fusion, translation, alignment, and co-learning, and how these approaches support clinical predictions and medical image analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is multimodal data especially important in clinical decision models?",{"text":80,"@type":76},"Biomedical research and clinical care naturally involve multiple data types, such as imaging sequences plus labs, demographics, ECG, and genetic expression, which reflect different representation spaces.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges remain despite advances in multimodal models?",{"text":84,"@type":76},"The survey highlights data biases and the scarcity of large, well-curated datasets in many biomedical domains, which limits performance and reliability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]