Michael Rutherford
Instructor
University of Arkansas for Medical Sciences
faculty
COM | Biomedical Informatics
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Biography and Research Information
OverviewAI-generated summary
Michael Rutherford's research focuses on the application of computational methods and data analysis to biomedical challenges. His work includes the development of software libraries for medical image synthesis, such as the Python library `medigan`, and the creation of datasets for evaluating medical image de-identification processes. Rutherford has also investigated the implementation of health data exchange standards, specifically the HL7 FHIR standard, to support electronic source data in clinical research. His recent publications also touch upon the analysis of caregiver burden expressed in social media discussions and the documentation of data de-identification procedures for AI in healthcare.
Rutherford has a publication record of 49 articles, with a total of 378 citations and an h-index of 12. He collaborates with several researchers at the University of Arkansas for Medical Sciences, including Christopher P. Wardell, Michael Bauer, Phillip Farmer, and Fred Prior.
Metrics
- h-index: 12
- Publications: 49
- Citations: 378
Selected Publications
- Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies (2026) DOI
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images (2025) DOI
- Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies (2025) DOI
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project (2024) DOI
- Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp> (2024) DOI
- 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) DOI
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects (2024) DOI
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project (2024) DOI
- Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research (2023) DOI
- medigan: a Python library of pretrained generative models for medical image synthesis (2023) DOI
- Analysis of Caregiver Burden Expressed in Social Media Discussions (2023) DOI
- 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) DOI
- 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) DOI
- A DICOM dataset for evaluation of medical image de-identification (2021) DOI
- Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research (2021) DOI
Grants & Funding
- TO4 Moonshot BioBank – Support to IROC NIH/Nat. Cancer Institute - Pass Through: Leidos Co-Investigator
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