Machine intelligence for radiation science - 67th Annual Meeting Symposium Summary

Sep 8, 2026

The era of high-throughput techniques has generated large-scale medical and research datasets, exposing critical bottlenecks in processing, analyzing, and interpreting information. 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.

LanguageEnglish

CategoryPresentations

FormatDOCX

Pages27

Reading Time9 min

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Introduction

Machine intelligence, machine learning, and AI definitions

Key challenges for MI integration in radiation research

Summary of the 67th Annual Meeting symposium presentations

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium Lydia 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 aDepartment of Radiation Oncology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105 bDepartment of Anesthesia and Critical Care Medicine, The Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, 19146 cNASA Ames Research Center dDivision of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom eStanford Center for Biomedical Informatics Research, Department of Medicine, 1265 Welch Road, Stanford, CA 94305 fDepartment of Biomedical Data Science, Stanford University, Palo Alto, CA, USA gThe Cavendish Laboratory, University of Cambridge hDept of Haematology, School of Clinical Medicine, University of Cambridge iKBR, NASA Ames Research Center jRadiology and Imaging Sciences, NIH Clinical Center, 10 Center Dr, Bethesda, MD 20892 kBionetics, NASA Ames Research Center. Moffett Field, CA, USA lDept. Of Physiology Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, UK mInstitute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK nInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, UK 0MRC Health Data Research UK (HDR UK) Midlands, UK pUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK qDepartment of Radiation Oncology, Section of Molecular Radiation Biology, UT Southwestern Medical Center, Dallas, TX, 75390 1All 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: HYPERLINK "mailto:lydia.wilson@stjude.org" \h lydia.wilson@stjude.org Running head: Machine intelligence for radiation science Keywords: Machine learning, artificial intelligence, radiobiology, radiotherapy, lung cancer, voxel-based analysis, ontology Abstract The 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. Introduction Recent 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

FAQs

What role does machine intelligence play in handling high-throughput medical data?

Machine intelligence addresses limitations in how large-scale datasets are processed, analyzed, and interpreted through adaptive, data-driven modeling.

How does the document distinguish MI, AI, and machine learning?

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.

Which topics are covered in the summarized symposium presentations?

The summary covers metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

Sep 8, 2026

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