Match tier
Listed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-08-15
M
Michael Rutherford
Instructor
Also affiliated: University of the West Indies (2010); South West London and St George's Mental Health NHS Trust (2012–2017); Manchester University NHS Foundation Trust (2021–2022); Conselleria de Sanitat Universal i Salut Pública (2012); Winthrop Rockefeller Foundation (2020–2023); Committee on Publication Ethics (2012); WWF-UK (2012); Wythenshawe Hospital (2020–2022); October 6 University (2012)
Faculty Researcher
COM | Biomedical Informatics
12 h-index
47 pubs
410 cited
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: 12
- Publications: 47
- Citations: 410
Selected Publications
-
Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies (2026)
-
Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images (2025)
-
Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies (2025)
-
New implementation of data standards for AI in oncology: Experience from the EuCanImage project (2024)
-
Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp> (2024)
-
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)
-
Documenting the de-identification process of clinical and imaging data for AI for health imaging projects (2024)
-
New implementation of data standards for AI in oncology. Experience from the EuCanImage project (2024)
-
Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research (2023)
-
medigan: a Python library of pretrained generative models for medical image synthesis (2023)
-
Analysis of Caregiver Burden Expressed in Social Media Discussions (2023)
-
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)
-
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)
-
A DICOM dataset for evaluation of medical image de-identification (2021)
-
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
8 shared publications
In database
- 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
Showing 5 of 8 shared publications
Jonathan P. Bona
University of Arkansas for Medical Sciences
4 shared publications
In database
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
- Analysis of Caregiver Burden Expressed in Social Media Discussions
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project
Karim Lekadir
Institució Catalana de Recerca i Estudis Avançats (ES)
4 shared publications
- 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
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project
Maryam Y. Garza
The University of Texas Health Science Center at San Antonio (US)
3 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- 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
Bhargav Adagarla
Clinical Research Institute (US)
3 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- 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
Eric L. Eisenstein
University of Victoria (CA)
3 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- 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
Kanecia O. Zimmerman
Clinical Research Institute (US)
3 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- 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)
3 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- 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
Phillip Farmer
University of Arkansas for Medical Sciences
3 shared publications
In database
- A DICOM dataset for evaluation of medical image de-identification
- 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
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
Keyvan Farahani
National Institutes of Health (US)
3 shared publications
- A DICOM dataset for evaluation of medical image de-identification
- 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
- Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research
Christopher P. Wardell
University of Arkansas for Medical Sciences
3 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
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
Maciej Bobowicz
Gdańsk Medical University (PL)
3 shared publications
- 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
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project
Karan R. Kumar
Clinical Research Institute (US)
2 shared publications
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research
- Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies
Kirk Smith
University of Arkansas for Medical Sciences (US)
2 shared publications
- A DICOM dataset for evaluation of medical image de-identification
- Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp>
Ulrike Wagner
Frederick National Laboratory for Cancer Research (US)
2 shared publications
- A DICOM dataset for evaluation of medical image de-identification
- Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research
Similar Researchers
Based on overlapping research topics
Trong-Thuan Nguyen
University of Arkansas at Fayetteville
Medical Imaging Techniques and Applications
Machine Learning Applications
Natural Language Processing Techniques
Yu Sun
University of Central Arkansas
Medical Imaging Techniques and Applications
Machine Learning Applications
Natural Language Processing Techniques
John M. Gauch
University of Arkansas at Fayetteville
Medical Imaging Techniques and Applications
Machine Learning Applications
Natural Language Processing Techniques
Minh Quan Tran
University of Arkansas at Fayetteville
Medical Imaging Techniques and Applications
Machine Learning Applications
Natural Language Processing Techniques
Rupesh Konduru
Southern Arkansas University
Medical Imaging Techniques and Applications
Machine Learning Applications
Computer Graphics and Visualization Techniques
Xin Li
University of Arkansas at Fayetteville
Medical Imaging Techniques and Applications
Machine Learning Applications
Bioinformatics and Genomic Networks