Pangiotis Korfiatis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Pangiotis Korfiatis has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Andrew D. Missert
Department of Radiology, Mayo Clinic, Rochester, USA
1 shared publication - Daniel J. Blezek
Departments of Radiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
1 shared publication - Lonny Olson-Williams
Mayo Clinic Rochester, Rochester, USA
1 shared publication
Latest Publications
- 22 October 2021
- Published by Springer Nature in Journal of Digital Imaging
- Vol. 34 (6) , 1435-1446
AI Integration in the Clinical Workflow
Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist’s workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease.
- 22 October 2021
- Published by Springer Nature in Journal of Digital Imaging
- Vol. 34 (6) , 1435-1446
AI Integration in the Clinical Workflow
Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist’s workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease.