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Machine intelligence (MI) methods address these limitations by enabling adaptive modeling of complex data. The 67th Annual Meeting of the Radiation Research Society presented a symposium focused on MI advancements, summarizing three talks spanning metadata processing and ontological formalization, data mining for pediatric oncology outcomes, and lung cancer imaging.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/machine-intelligence-for-radiation-science-67th-annual-meeting-symposium-summary/219171/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/machine-intelligence-for-radiation-science-67th-annual-meeting-symposium-summary/219171.png","ImageObject",442,249,{"name":88,"@type":89},"Theodore","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/vnd.openxmlformats-officedocument.wordprocessingml.document","2026-10-29","2026-09-08",true,{"@type":98,"interactionType":99,"userInteractionCount":8},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What role does machine intelligence play in handling high-throughput medical data?","Question",{"text":108,"@type":109},"Machine intelligence addresses limitations in how large-scale datasets are processed, analyzed, and interpreted through adaptive, data-driven modeling.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the document distinguish MI, AI, and machine learning?",{"text":113,"@type":109},"AI is described as a broader definition for systems that mimic behavior, while MI is used more specifically for computational mimicry. Machine learning is a subset that builds models from data and improves with iteration.",{"name":115,"@type":106,"acceptedAnswer":116},"Which topics are covered in the summarized symposium presentations?",{"text":117,"@type":109},"The summary covers metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},219171,1791366224,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":8,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":141},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium\nLydia J Wilsona,1, Frederico C Kifferb, Daniel C. Berriosc, Abigail Bryce-Atkinsond, Sylvain V. Costesc, Olivier Gevaerte,f, Bruno F. E. Matarèseg,h, Jack Millerc,i, Pritam Mukherjeee,j, Kristen Peachk, Paul N. Schofieldl, Luke T. Slaterm-p, and Britta Langenq\naDepartment of Radiation Oncology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105\nbDepartment of Anesthesia and Critical Care Medicine, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, 19146\ncNASA Ames Research Center\ndDivision of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom\neStanford Center for Biomedical Informatics Research, Department of Medicine, 1265 Welch Road, Stanford, CA 94305\nfDepartment of Biomedical Data Science, Stanford University, Palo Alto, CA, USA\ngThe Cavendish Laboratory, University of Cambridge\nhDept of Haematology, School of Clinical Medicine, University of Cambridge\niKBR, NASA Ames Research Center\njRadiology and Imaging Sciences, NIH Clinical Center, 10 Center Dr, Bethesda, MD 20892\nkBionetics, NASA Ames Research Center. Moffett Field, CA, USA\nlDept. Of Physiology Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, UK\nmInstitute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK\nnInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, UK\n0MRC Health Data Research UK (HDR UK) Midlands, UK\npUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK\nqDepartment of Radiation Oncology, Section of Molecular Radiation Biology, UT Southwestern Medical Center, Dallas, TX, 75390\n1All correspondence to Lydia J Wilson, PhD, Department of Radiation Oncology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, MS210, Memphis, TN 38105, USA; tel: +1 901 595 1895; email: \u0013 HYPERLINK \"mailto:lydia.wilson@stjude.org\" \\h \u0014lydia.wilson@stjude.org\u0015\nRunning head: Machine intelligence for radiation science\nKeywords: Machine learning, artificial intelligence, radiobiology, radiotherapy, lung cancer, voxel-based analysis, ontology\nAbstract\nThe era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.\nIntroduction\nRecent decades have seen machine intelligence (MI) approaches become more widely used in basic research and clinical applications. This rapid advancement was made possible through the convergence of several factors, most notably easier access to information technologies, increase in availability of machine learning (ML) algorithms across all popular programming languages, breakthroughs in instrumentation for data acquisition, decline in wet lab and computational hardware costs, and a new generation of scientists and medical professionals trained in bioinformatics techniques. The terms MI and artificial intelligence (AI) are often used interchangeably and describe a technology that enables a machine to (convincingly) mimic human behavior, AI being a broader definition applicable to any system, whereas MI is specific to computational mimicry. ML is a specific subset of this field characterized by algorithms that process large quantities of data to build an analytical ","cbCaia2aM7BeTNos","https://ap.wps.com/l/cbCaia2aM7BeTNos","docx",1123397,27,"English","# Introduction\n## Machine intelligence, machine learning, and AI definitions\n## Key challenges for MI integration in radiation research\n## Summary of the 67th Annual Meeting symposium presentations","[{\"question\":\"What role does machine intelligence play in handling high-throughput medical data?\",\"answer\":\"Machine intelligence addresses limitations in how large-scale datasets are processed, analyzed, and interpreted through adaptive, data-driven modeling.\"},{\"question\":\"How does the document distinguish MI, AI, and machine learning?\",\"answer\":\"AI is described as a broader definition for systems that mimic behavior, while MI is used more specifically for computational mimicry. Machine learning is a subset that builds models from data and improves with iteration.\"},{\"question\":\"Which topics are covered in the summarized symposium presentations?\",\"answer\":\"The summary covers metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.\"}]","Machine intelligence for radiation science - 67th Annual Meeting Symposium Summary | DOCX",1788862014,9]