Match tier
Listed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-05
M
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Michael Rutherford is an instructor in Biomedical Informatics at the University of Arkansas for Medical Sciences. His research focuses on the synthesis and application of generative models for medical image data, as demonstrated by his work on the Python library "medigan." Rutherford has also investigated methods for de-identifying clinical and imaging data to support artificial intelligence in healthcare projects and has published on the evaluation of HL7 FHIR standard implementations for eSource data exchange in clinical research. His work also extends to analyzing caregiver burden expressed in social media discussions. Rutherford's scholarship metrics include an h-index of 12, with 47 total publications and 400 total citations.
Metrics
- h-index: 10
- Publications: 31
- Citations: 360
Positions
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Instructor 2014–presentUniversity of Arkansas for Medical Sciences COM | Biomedical Informatics Institutional directory
Selected Publications
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Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies (2026)
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Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images (2025)
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Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies (2025)
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New implementation of data standards for AI in oncology: Experience from the EuCanImage project (2024)
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Linking The Cancer Imaging Archive and GenBank to the National Clinical Cohort Collaborative (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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New implementation of data standards for AI in oncology. Experience from the EuCanImage project (2024)
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Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research (2023)
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medigan: a Python library of pretrained generative models for medical image synthesis (2023)
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Analysis of Caregiver Burden Expressed in Social Media Discussions (2023)
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Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups (2021)
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High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse (2021)
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A DICOM dataset for evaluation of medical image de-identification (2021)
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Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research (2021)
Grants & Funding
As listed on this researcher's institutional profile.
- TO4 Moonshot BioBank – Support to IROC NIH/Nat. Cancer Institute via Leidos Co-Investigator
Collaboration Network
Top Collaborators
Fred Prior
University of Arkansas for Medical Sciences
9 shared publications
In database
- Chest imaging representing a COVID-19 positive rural U.S. population
- 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
Showing 5 of 9 shared publications
Christopher P. Wardell
University of Arkansas for Medical Sciences
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
Showing 5 of 6 shared publications
Phillip Farmer
University of Arkansas for Medical Sciences
6 shared publications
In database
- Chest imaging representing a COVID-19 positive rural U.S. population
- A DICOM dataset for evaluation of medical image de-identification
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
Showing 5 of 6 shared publications
Michael Bauer
University of Arkansas for Medical Sciences
5 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Cody Ashby
University of Arkansas for Medical Sciences
5 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Eileen M. Boyle
NYU Langone Health (US)
5 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Brian A. Walker
Indiana University Health (US)
5 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Carolina Schinke
University of Arkansas for Medical Sciences
4 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Sharmilan Thanendrarajan
University of Arkansas for Medical Sciences
4 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Frits van Rhee
University of Arkansas for Medical Sciences
4 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Maurizio Zangari
University of Arkansas for Medical Sciences
4 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Gareth J. Morgan
NYU Langone Health (US)
4 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Faith E. Davies
NYU Langone Health (US)
4 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Extracting Prognostic Molecular Information from PET-CT Imaging of Multiple Myeloma Using Radiomic Approaches
- Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior
Maryam Y. Garza
The University of Texas at San Antonio Health Science Center (US)
4 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- Evaluating the Coverage of the HL7 ® FHIR ® Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies.
- Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies
- Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies
Ümit Topaloĝlu
Wake Forest University (US)
4 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- Evaluating the Coverage of the HL7 ® FHIR ® Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies.
- Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies
- Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies
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