Fred Prior
Distinguished Professor
Also affiliated: Florida State University (2005–2006); Boston University (2015); Leidos (United States) (2015); Ulsan College (2024); United States Department of Health and Human Services (2024); National Institutes of Health (1997); Arkansas Children's Hospital (2025); Pennsylvania State University (1993–2003); Regenstrief Institute (1998); Siemens (Germany) (1989); Philips (Finland) (1997–1998); University of Crete (2024); Bayer (United States) (1998); University of the Basque Country (2025); Duke University (1998); University of California, San Francisco (2024); Washington University in St. Louis (2007–2015); University of Arkansas Medical Center (2018–2025); Hospital of the University of Pennsylvania (1997); William Penn University (1993); University of California System (2024); University of Ulsan (2024); Mallinckrodt (United States) (2005–2016); National Center for Biotechnology Information (1997); Collaborative Research Group (2024); Philips (Netherlands) (1998); Duke Medical Center (1997); Frederick National Laboratory for Cancer Research (2015); Philips (United States) (1998–2002); Siemens (United States) (1989–1992); University Hospital of Heraklion (2024); Arista (United States) (1998); Arkansas Children's Nutrition Center (2025); University Radiology (2012); Duke University Hospital (1997); Pusat Penelitian Arkeologi Nasional (2005–2006); IPS Research (United States) (1998); Cohort (United Kingdom) (2024); New York University (2024); Case Western Reserve University (1977); University of Nebraska Medical Center (2023); Optica (2013); University of Pennsylvania (2002–2024); Penn State Milton S. Hershey Medical Center (1993–1997); The University of Texas Southwestern Medical Center (2024); Mallinckrodt (Ireland) (2005–2010); University of New England (2005–2006); Stanford University (2024)
Faculty Researcher
Department Chairs, College of Medicine
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Fred Prior's research focuses on the application of artificial intelligence and machine learning in medical imaging, with a particular emphasis on cancer detection and diagnosis. He has published extensively on topics such as AI and machine learning in cancer imaging, checklists for AI in medical imaging, and guidelines for trustworthy AI in healthcare. His work also addresses the crucial aspects of data preparation and infrastructure for AI in medical imaging, exploring open-access platforms and the experiences of international projects.
Prior's research extends to the application of machine learning in other critical healthcare settings, including intensive care units, and the use of AI for processing voice samples to identify neurological conditions like Parkinson's disease. He is also involved in developing tools and libraries, such as medigan, for synthesizing medical images using generative models. His scholarship is recognized by a high h-index of 36 and over 13,000 citations, reflecting a significant body of work in the field.
He has secured substantial federal funding for his research, including a $7.8 million NIH grant for a Data Coordinating and Operations Center for a pediatric clinical trials network and a $1.5 million NIH grant for sustaining platforms for quantitative imaging informatics in precision medicine. Prior holds leadership positions in various institutional committees and serves as an editor for prominent biomedical informatics journals, demonstrating his engagement with the broader research community.
Research Overview
Member/Co-Chair NIH Neuroscience and Ophthalmic Imaging Technologies Study Section Member of the Winthrop P. Rockefeller Cancer Institute Senior Leaders committee Member of the COM Research Council Member of the Research Technology Executive Committee Member, Electronic Data Warehouse Working and Executive Governance Groups Member, Translational Research Institute Leadership Council Leader, Biomedical Informatics Core, Translational Research Institute Special Issue Editor, Journal of Biomedical Informatics Reviewing Editor, Computerized Medical Imaging and Graphics Associate Editor, IEEE Journal of Biomedical and Health Informatics
Metrics
- h-index: 37
- Publications: 225
- Citations: 14,576
Selected Publications
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Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models (2026)
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Harnessing Native-Resolution 2D Embeddings for Lung Cancer Classification: A Feasibility Study with the RAD-DINO Self-supervised Foundation Model (2025)
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An Activation Likelihood Estimation Meta-Analysis of Voxel-Based Morphometry Studies of Chemotherapy-Related Brain Volume Changes in Breast Cancer (2025)
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Enabling global image data sharing in the life sciences (2025)
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A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations (2025)
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Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples (2025)
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FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2025)
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Environment scan of generative AI infrastructure for clinical and translational science (2025)
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New implementation of data standards for AI in oncology: Experience from the EuCanImage project (2024)
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Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples (2024)
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A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges (2024)
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Implementing Data Governance with Multi-Modal Privacy-Preserving Record Linkages between Restricted and Public Open Enclaves (2024)
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Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification (2024)
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Documenting the de-identification process of clinical and imaging data for AI for health imaging projects (2024)
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Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson’s disease (2024)
Federal Grants 2 $9,468,038 total
Data Coordinating and Operations Center for the ECHO IDeA States Pediatric Clinical Trials Network
Grants & Funding
As listed on this researcher's institutional profile. Federal awards with verified records are shown above.
- TCIA Sustainment and Scalability - Platforms for Quantitative Imaging Informatics in Precision Medicine - Year 4 - Continuation NIH/Nat. Cancer Institute Principal Investigator
- AcetaSTAT Validation and Commercialization NIH/Nat. Inst. of Diabetes & Digestive & Kidney Diseases via Acetaminophen Toxicity Diagnostics, LLC Principal Investigator
- Pediatric Head Models for Improved Imaging of Neurological Development NIH/National Institutes of Health via Electrical Geodesics, Inc. Co-Investigator
- Integrated Versus Referral Care for Complex Psychiatric Disorders in Rural FQHCs - Continuation - Continuation - Continuation - Continuation Patient-Centered Outcomes Research Institute via University of Washington Principal Investigator
- Robust and Trusted Data Analytics (DART) National Science Foundation via Arkansas Economic Development Commission Principal Investigator
- Pragmatic Randomized Trial of Proton vs. Photon Therapy for Patients with Non-Metastatic Breast Cancer Receiving Comprehensive Nodal Radiation: A Radiotherapy Comparative Effectiveness (RADCOMP) Trial - Continuation Patient-Centered Outcomes Research Institute via Washington University Principal Investigator
- Biomarkers for Charcot Arthropathy in Diabetic Patients NIH Principal Investigator
- Cancer Center of Nanotechnology Excellence - Continuation - Continuation - Continuation - Continuation NIH/Nat. Cancer Institute via Washington University Principal Investigator
- Pragmatic Randomized Trial of Proton vs. Photon Therapy for Patients with Non-Metastatic Breast Cancer Receiving Comprehensive Nodal Radiation: A Radiotherapy Comparative Effectiveness (RADCOMP) Trial Patient-Centered Outcomes Research Institute via Washington University Principal Investigator
- TO3 TCIA NLST SupportL Radiology/Pathology NIH/Nat. Cancer Institute via Leidos Principal Investigator
- Tools for data curation, quality control and data interoperability European Commission via Universitat de Barcelona Principal Investigator
- Resources for development and validation of Radiomic analyses & Adaptive Therapy NIH Principal Investigator
- TCIA TO7 Apollo NIH/Nat. Cancer Institute via Leidos Principal Investigator
- HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCES NIH Principal Investigator
- GPU COMPUTING RESOURCE TO ENABLE INNOVATION IN IMAGING AND NETWORK BIOLOGY NIH Principal Investigator
- DART GRA -Ussery year 5 National Science Foundation via Arkansas Economic Development Commission Principal Investigator
- DART GRA -Ussery year 4 National Science Foundation via Arkansas Economic Development Commission Principal Investigator
- EPSCoR - CASE Summer National Science Foundation via Arkansas Economic Development Commission Principal Investigator
- Robust and Trusted Data Analytics National Science Foundation via Arkansas Economic Development Commission Principal Investigator
- RFP S16-011 (#4) NIH/Nat. Cancer Institute via Leidos Principal Investigator
- Expanding Translational Research in Arkansas NIH Co-Investigator
- DART Summer Undergraduate Research Experience (SURE) Arkansas Economic Development Commission Principal Investigator
- Pragmatic Randomized Trial of Proton vs. Photon Therapy for Patients with Non-Metastatic Breast Cancer Receiving Comprehensive Nodal Radiation: A Radiotherapy Comparative Effectiveness (RADCOMP) Trial - Continuation - Continuation Patient-Centered Outcomes Research Institute via Washington University Principal Investigator
- Engaging Cooperative Sites for Trial Acceleration, Trust, Innovation, and Capability (ECSTATIC) NIH/National Center for Advancing Translational Sciences via Vanderbilt University Principal Investigator
- ACT Wave Site Consortium Agreement with University of Pittsburgh CTSI - Continuation NIH/National Center for Advancing Translational Sciences via University of Pittsburgh Co-Investigator
- Data Coordinating and Operations Center (DCOC) for the IDeA States Pediatric Clinical Trials Network NIH Co-Investigator
- TO4 Moonshot BioBank – Support to IROC NIH/Nat. Cancer Institute via Leidos Principal Investigator
Collaboration Network
Top Collaborators
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
- Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures
Showing 5 of 9 shared publications
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures
- API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
Showing 5 of 8 shared publications
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures
- API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
Showing 5 of 7 shared publications
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
- Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
- medigan: a Python library of pretrained generative models for medical image synthesis
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project
Showing 5 of 6 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures
- Vitamin D Oral Replacement in Children With Obesity Related Asthma: <scp>VDORA1</scp> Randomized Clinical Trial
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Showing 5 of 6 shared publications
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)
- Gait Declines Differentially in, and Improves Prediction of, People with Parkinson’s Disease Converting to a Freezing of Gait Phenotype
Showing 5 of 6 shared publications
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)
- Gait Declines Differentially in, and Improves Prediction of, People with Parkinson’s Disease Converting to a Freezing of Gait Phenotype
Showing 5 of 6 shared publications
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)
- Gait Declines Differentially in, and Improves Prediction of, People with Parkinson’s Disease Converting to a Freezing of Gait Phenotype
Showing 5 of 6 shared publications
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
- DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review
- 474 Innovative solutions to streamline data collection, exchange, and utilization in translational research
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review
- medigan: a Python library of pretrained generative models for medical image synthesis
- A DICOM dataset for evaluation of medical image de-identification
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
- Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Feasibility of telemedicine research visits in people with Parkinson’s disease residing in medically underserved areas
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES)
- Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson’s disease
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
- A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project
- Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures
- API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
- API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
- Vitamin D Oral Replacement in Children With Obesity Related Asthma: <scp>VDORA1</scp> Randomized Clinical Trial
- 474 Innovative solutions to streamline data collection, exchange, and utilization in translational research
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