[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126858-en":3,"doc-seo-126858-105":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},126858,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Multimodal Machine Learning in Image-Based and Clinical Biomedicine - Survey and Prospects","Machine learning applications in medical AI have moved from traditional statistical methods toward deeper deep learning models. This survey maps the current landscape of multimodal ML and its impact on medical image analysis and clinical decision support, emphasizing representation, fusion, translation, alignment, and co-learning. The work outlines persistent challenges such as data bias and limited large-scale biomedical datasets, while describing innovations and the need for principled assessment and practical deployment that supports integration into routine clinical practice.","arXiv :2311 .02332v5 [ cs .LG] 20 Jan 2024  \nMultimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects  \nElisa Warner 1 , Joonsang Lee 1 , William Hsu2 , Tanveer Syeda-Mahmood3 , Charles E Kahn, Jr.4 ,  \nOlivier Gevaert5 , Arvind Rao 1  \n1 Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, 100 Washtenaw Ave, Ann Arbor, 48109, MI,  \nUSA.  \n2 Department of Medical & Imaging Informatics, University of California Los Angeles, 924 Westwood Blvd Ste 420, Los Angeles, 90024, CA,  \nCountry.  \n3 Almaden Research Center, IBM, 650 Harry Rd, San Jose, 95120, CA, USA.  \n4 Department of Radiology, University of Pennsylvania, 3400 Spruce St. , Philadelphia, 19104, PA, USA.  \n5 Center for Biomedical Informatics Research, Stanford, 1265 Welch Road, Stanford, 94305, CA, USA.  \nContributing authors: [elisawa@umich.edu](elisawa@umich.edu) ; [leejoons@umich.edu](leejoons@umich.edu) ;  \n[whsu@mednet.ucla.edu](whsu@mednet.ucla.edu) ; [stf@us.ibm.com](stf@us.ibm.com) ; [ckahn@upenn.edu](ckahn@upenn.edu) ;  \n[ogevaert@stanford.edu](ogevaert@stanford.edu) ; [ukarvind@med.umich.edu](ukarvind@med.umich.edu) ;  \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 colearning, 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.  \n1  \nDespite 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,  \nalignment, co-learning, artificial intelligence, data integration  \n1 Introduction  \nMachine learning (ML), the process of leveraging algorithms and optimization to infer strategies for solving learning tasks, has enabled some of the greatest developmentsin 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 time, the importance of both plentiful and well-curated data has become better understood, coinciding as of the time of writing this article with the incredible premise of OpenAI’s ChatGPT and GPT-4 engines as well as other generative AI models which are trained on a vast, well-curated, and diverse array of content from across the internet [1] .  \nAs more data has become available, a wider selection of datasets containing more than one modality has also enabled growth in the multimodal research sphere. Multimodal data is intrinsic to biomedical research and clinical care. While data belonging to a single modality can be conceptualized as a way in which something is perceived or captured in the world into an abstract digitized representation such as a waveform or image, multimodal data aggregates multiple modalities and thus consists of several intrinsically different representation spaces (","cbCaihFS9mUShdcJ","https://ap.wps.com/l/cbCaihFS9mUShdcJ","pdf",2941924,1,30,"English","en",105,"# Introduction\n## Multimodal data in biomedical research and clinical care\n## Conceptual value of multimodal models\n## Challenges in multimodal learning","[{\"question\":\"What does the survey focus on regarding multimodal ML in biomedicine?\",\"answer\":\"The survey focuses on the current landscape of multimodal machine learning and its impact on medical image analysis and clinical decision support, highlighting representation, fusion, translation, alignment, and co-learning.\"},{\"question\":\"Why is multimodal data important in clinical decision models?\",\"answer\":\"Multimodal data combines intrinsically different representation spaces, such as imaging modalities (CT, PET, MRI sequences) and non-imaging modalities like blood tests, demographics, ECG, and genetic expression.\"},{\"question\":\"What key challenges does the paper emphasize for multimodal biomedical ML?\",\"answer\":\"The paper emphasizes challenges including data biases and the scarcity of large-scale (“big data”) resources in many biomedical domains.\"}]","Multimodal Machine Learning in Image-Based and Clinical Biomedicine - Survey and Prospects | PDF",1785935263,76,{"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/126858/",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 does the survey focus on regarding multimodal ML in biomedicine?","Question",{"text":75,"@type":76},"The survey focuses on the current landscape of multimodal machine learning and its impact on medical image analysis and clinical decision support, highlighting representation, fusion, translation, alignment, and co-learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is multimodal data important in clinical decision models?",{"text":80,"@type":76},"Multimodal data combines intrinsically different representation spaces, such as imaging modalities (CT, PET, MRI sequences) and non-imaging modalities like blood tests, demographics, ECG, and genetic expression.",{"name":82,"@type":73,"acceptedAnswer":83},"What key challenges does the paper emphasize for multimodal biomedical ML?",{"text":84,"@type":76},"The paper emphasizes challenges including data biases and the scarcity of large-scale (“big data”) resources in many biomedical domains.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]