[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125427-en":3,"doc-seo-125427-105":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},125427,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning - Tutorial","Deep learning–based clinical imaging analysis drives diagnostic artificial intelligence that can match or exceed clinical experts and transform medical practice. Automated machine learning (autoML) platforms reduce the technical barrier to deep learning, extending capabilities to clinicians with limited expertise and to more autonomous multimodal foundation models. This tutorial provides a technical overview of autoML and describes applications in education, research, and clinical care. It outlines the full workflow, from data acquisition and partitioning to training, validation, analysis, and deployment, while emphasizing ethical and technical best practices and comparing code-free, code-minimal, and code-intensive options.","JOURNAL OF MEDICAL INTERNET RESEARCH Thirunavukarasu et al  \nTutorial  \nDemocratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial  \n\n| Arun James Thirunavukarasu1,2, BA, MB BChir; Kabilan Elangovan2, BEng; Laura Gutierrez2, MD; Yong Li2, MD; Iris Tan2, BEng; Pearse A Keane3, MD; Edward Korot4,5, MD; Daniel Shu Wei Ting2,4,6, PhD |\n| --- |\n| 1University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom\u003Cbr>2Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore 3Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom\u003Cbr>4Byers Eye Institute, Stanford University, Palo Alto, CA, United States 5Retina Specialists of Michigan, Grand Rapids, MI, United States 6Singapore National Eye Centre, Singapore, Singapore\u003Cbr>Corresponding Author:\u003Cbr>Arun James Thirunavukarasu, BA, MB BChir University of Cambridge School of Clinical Medicine Addenbrooke's Hospital\u003Cbr>Hills Rd\u003Cbr>Cambridge, CB2 0SP United Kingdom Phone: 44 01223 336700\u003Cbr>[Email:](Email: ajt205@cantab.ac.uk)[ ](Email: ajt205@cantab.ac.uk)[ajt205@cantab.ac.uk](Email: ajt205@cantab.ac.uk)\u003Cbr>Abstract |\n| Deep learning–based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling “hands-on” education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects ofAI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.\u003Cbr>(J Med Internet Res 2023;25:e49949) doi:  10.2196/49949 |\n\nKEYWORDS  \nmachine learning; automated machine learning; autoML; artificial intelligence; democratization; autonomous AI; imaging; image analysis; automation; AI engineering  \nIntroduction  \nAutomated machine learning (autoML) is the product of attempts to broaden artificial intelligence (AI) engineering capability beyond those with technical and computational expertise [1] . Machine learning (ML) is a form of AI that  \ndescribes the computational process of leveraging data to improve performance in a defined task, thereby developing sophisticated models without explicit programming. More recently, deep learning (DL) has emerged as a powerful form of ML capable of interpreting unstructured data, such as images, language, and speech [2,3] . In DL, layers of representation are  \n[https://www.jmir.org/2023/1/e49949](https://www.jmir.org/2023/1/e49949)  \nXSL• FO  \nRenderX  \nJ Med Internet Res 2023 | vol. 25 | e49949 | p. 1 (page numbe","cbCaikPLgKQnQYZe","https://ap.wps.com/l/cbCaikPLgKQnQYZe","pdf",426752,1,11,"English","en",105,"# Introduction\n## Automated machine learning and democratization of AI\n## Deep learning in clinical imaging\n## Potential clinical value and deployment considerations\n# Tutorial focus (autoML workflow)\n## Data acquisition and partitioning\n## Model training and validation\n## Analysis and model deployment\n## Platform coding requirements (code-free to code-intensive)","[{\"question\":\"What problem does autoML address in clinical imaging AI development?\",\"answer\":\"AutoML broadens access to AI engineering by reducing the need for specialized technical and computational expertise, enabling imaging-based deep learning workflows for a wider audience.\"},{\"question\":\"How does the tutorial structure the autoML project workflow?\",\"answer\":\"It outlines key stages including data acquisition, data partitioning, model training, model validation, analysis, and model deployment.\"},{\"question\":\"Why can autoML contribute to safer and more effective clinical practice?\",\"answer\":\"The tutorial connects clinical improvements to accuracy, speed, and reproducibility of deep learning models, which can complement skilled human assessments and support robust systems when properly validated and deployed.\"}]","Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning - Tutorial | PDF",1785898852,28,{"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},"democratizing-artificial-intelligence-imaging-analysis-with-automated-machine-learning-tutorial","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/democratizing-artificial-intelligence-imaging-analysis-with-automated-machine-learning-tutorial/125427/",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 problem does autoML address in clinical imaging AI development?","Question",{"text":75,"@type":76},"AutoML broadens access to AI engineering by reducing the need for specialized technical and computational expertise, enabling imaging-based deep learning workflows for a wider audience.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the tutorial structure the autoML project workflow?",{"text":80,"@type":76},"It outlines key stages including data acquisition, data partitioning, model training, model validation, analysis, and model deployment.",{"name":82,"@type":73,"acceptedAnswer":83},"Why can autoML contribute to safer and more effective clinical practice?",{"text":84,"@type":76},"The tutorial connects clinical improvements to accuracy, speed, and reproducibility of deep learning models, which can complement skilled human assessments and support robust systems when properly validated and deployed.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]