Proceedings of a Symposium
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This activity was supported by a contracts from the American College of Radiology, U.S. Department of Energy (Award #DE-EM0005239 and Award #DE-HS0000031), the Gordon and Betty Moore Foundation (Grant ID# 12565), the National Academy of Sciences Thomas Lincoln Casey Fund, the Richard Lounsbery Foundation, and the U.S. Nuclear Regulatory Commission (Award #31310024M0056). Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.
International Standard Book Number-13: 978-0-309-99459-0
Digital Object Identifier: https://doi.org/10.17226/29200
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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2025. Gilbert W. Beebe Symposium: AI and ML Applications in Radiation Therapy, Medical Diagnostics, and Radiation Occupational Health and Safety: Proceedings of a Symposium. Washington, DC: National Academies Press. https://doi.org/10.17226/29200.
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SYMPOSIUM PLANNING COMMITTEE1
LEO CHIANG (Co- Chair), The Dow Chemical Company
SHAHEEN A. DEWJI (Co- Chair), Georgia Institute of Technology
CAROLINE CHUNG, The University of Tex as MD Anderson Cancer Center
SYLVAIN V. COSTES, National Aeronautics and Space Administration
ANYI LI, Memorial Sloan Kettering Cancer Center
CEFERINO OBCEMEA, National Cancer Institute
National Academies of Sciences, Engineering, and Medicine Staff
DANIEL J. MULROW, Project Director and Program Officer
FRANCIS AMANKWAH, Senior Program Officer
CHARLES D. FERGUSON, Senior Board Director
DARLENE GROS, Senior Program Assistant
LAURA LLANOS, Financial Business Partner
___________________
1 The National Academies of Sciences, Engineering, and Medicine’s symposium planning committees are solely responsible for organizing the symposium, identifying topics, and choosing speakers. The responsibility for the published Proceedings of a Symposium rests with the rapporteurs and the institution.
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Reviewers
This Proceedings of a Symposium was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review comments and draft manuscript remain confidential to protect the integrity of the process.
We thank the following individuals for their review of this proceedings:
DANIELLE BITTERMAN, Dana-Farber Cancer Institute
SYLVAIN COSTES, National Aeronautics and Space Administration (former)
AMANDA SHEHU, George Mason University
Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse content of the proceedings nor did they see the final draft before its release. The review of this report was overseen by JOE W. GRAY (NAM), Oregon Health and Science University. He was responsible for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the rapporteurs and the National Academies.
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Contents
Organization of the Proceedings
2 INSIGHTS FROM THE ARTIFICIAL INTELLIGENCE FIELD
Transformational AI Opportunities in Healthcare
Promoting a Safe and Effective Clinical Environment for AI
3 THE POTENTIAL FOR USE OF ARTIFICIAL INTELLIGENCE IN RADIATION HEALTH FIELDS
Radiation Therapy and Oncology
4 DATA FOR ARTIFICIAL INTELLIGENCE READINESS
Data for AI: The Critical Role of Metadata and Context
Requisites and Challenges in Quantitative Imaging
Centralized Imaging Collaborations for AI Readiness
Data Management Tools and Strategy for Responsible Imaging AI
Digital Twins and Artificial Intelligence for Precision Medicine
Multiscale Digital Twins for Personalized Radiopharmaceutical Therapy
Digital Twins for Disease Modeling and Drug Development
6 MULTIMODAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Finding Use Cases for Multimodal AI in Radiology
AI and Multimodal Modeling for Lung Cancer Treatment
Integrating Mechanistic and ML Models to Assess Causal Effects of Radiotherapy on Patient Outcomes
The Promise and Challenge of Deep Learning in Radiation Risk Assessments
7 BIAS, ETHICS, AND REGULATORY ISSUES
Issues in the Use of AI for Breast Cancer Screening
Addressing Bias in AI-Driven Medical Imaging: Pitfalls and Best Practices
Shortcuts Causing Bias in Medical Imaging
8 TRUSTWORTHINESS AND TRANSPARENCY IN ARTIFICIAL INTELLIGENCE
Blood Pressure Modeling With Physics-Informed Neural Networks
Assessing Uncertainty in Indoor Radon Exposure Estimates: Implications for Radiation Epidemiology
AI-Enabled Healthcare Supply Chain Resilience and Risk Management
Acronyms and Abbreviations
| AAPM | American Association of Physicists in Medicine |
| ACR | American College of Radiology |
| AI | artificial intelligence |
| ARCH-AI | American College of Radiology Recognized Center for Healthcare-AI |
| ARPA-H | Advanced Research Projects Agency for Health |
| ART | adaptive radiotherapy |
| AUTOMAP | Automated Transform by Manifold Approximation |
| C-HER | Centralized Health and Exposomic Resource |
| CFR | Council on Foreign Relations |
| CNN | convolutional neural network |
| CNSC | Canadian Nuclear Safety Commission |
| CT | computed tomography |
| DICOM | Digital Imaging and Communications in Medicine |
| DNN | deep neural network |
| eGFR | estimated glomerular filtration rate |
| EHR | electronic health record |
| EHRLICH | Electronic Health Record–Informed Lagrangian Method for Precision Public Health |
| EKG | electrocardiogram |
| ERR | excess relative risk |
| FAS | Federation of American Scientists |
| FDA | Food and Drug Administration |
| FFR | fractional flow reserve |
| GDPR | General Data Protection Regulation |
| GPT | Generative Pretrained Transformer |
| Gy | gray, unit of absorbed radiation dose |
| HCI | human–computer interaction |
| HEDIS | Healthcare Effectiveness Data and Information Set |
| HITI | Healthcare Artificial Intelligence Innovation and Translational Informatics |
| J&J | Johnson & Johnson |
| LLM | large language model |
| mGy | milligray |
| MIDRC | Medical Imaging and Data Resource Center |
| MIT | Massachusetts Institute of Technology |
| ML | machine learning |
| MOSSAIC | Making Outcomes Using Surveillance Data and Scalable Artificial Intelligence for Cancer |
| MR | magnetic resonance |
| MRI | magnetic resonance imaging |
| NASA | National Aeronautics and Space Administration |
| NCI | National Cancer Institute |
| NICE | National Institute for Health and Care Excellence |
| NIH | National Institutes of Health |
| NLP | nonlinear parametric |
| NRC | Nuclear Regulatory Commission |
| NRSB | Nuclear and Radiation Studies Board |
| OCT | optical coherence tomography |
| PET | positron emission tomography |
| PINN | physics-informed neural network |
| PSMA | Prostate-Specific Membrane Antigen |
| QIB | quantitative imaging biomarker |
| QIBA | Quantitative Imaging Biomarkers Alliance |
| QMIC | Quantitative Medical Imaging Coalition |
| R&D | research and development |
| RCT | randomized controlled trial |
| RERF | Radiation Effects Research Foundation |
| RPT | radiopharmaceutical therapy |
| SciML | scientific machine learning |
| SEER | Surveillance, Epidemiology, and End Results |
| SHAP values | Shapley Additive Explanations |
| SNR | signal-to-noise ratio |
| T | tesla, unit of magnetic flux density |
| UCSD | University of California, San Diego |
| UNC | University of North Carolina |
| UT | University of Texas |
| VA | Veterans Affairs |
| VVUQ | verification, validation, and uncertainty quantification |
| XAI | explainable artificial intelligence |
| ZCTA | zip code tabulation area |
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