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Listed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-08-18
G
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Giridhar Dasegowda's research focuses on the application of artificial intelligence (AI) in medical imaging, particularly in radiology. His work addresses the practical challenges of integrating AI tools into clinical workflows, emphasizing the trustworthiness and validation of these systems. Dasegowda has investigated AI models for identifying suboptimal chest radiographs and reducing missed findings, contributing to improved diagnostic accuracy. His publications also explore the underlying principles of AI trustworthiness and the potential of advanced imaging techniques, such as photon counting computed tomography, to mitigate artifacts in chest CT scans. Dasegowda has also examined the use of no-code machine learning for medical image classification, demonstrating its utility in applications like identifying clavicle fractures.
Metrics
- h-index: 8
- Publications: 31
- Citations: 179
Selected Publications
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Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study (2025)
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Abstract No. 294 Analysis of Non-Tunneled Central Venous Catheter Placement Trends Among Medicare Patients (2010-2022): Procedural Volume, Specialty Involvement, and Reimbursement Patterns (2024)
Collaboration Network
Top Collaborators
Ajeet Kumar Chaurasia
University of Arkansas for Medical Sciences (US)
1 shared publication
- Abstract No. 294 Analysis of Non-Tunneled Central Venous Catheter Placement Trends Among Medicare Patients (2010-2022): Procedural Volume, Specialty Involvement, and Reimbursement Patterns
Shalini Singh
1 shared publication
- Abstract No. 294 Analysis of Non-Tunneled Central Venous Catheter Placement Trends Among Medicare Patients (2010-2022): Procedural Volume, Specialty Involvement, and Reimbursement Patterns
R. Purushothaman
University of Arkansas for Medical Sciences (US)
1 shared publication
- Abstract No. 294 Analysis of Non-Tunneled Central Venous Catheter Placement Trends Among Medicare Patients (2010-2022): Procedural Volume, Specialty Involvement, and Reimbursement Patterns
Narukulla Dadeepya Krishna
Massachusetts General Hospital (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Emiliano Garza-Frias
Harvard University (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Parisa Kaviani
Harvard University (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Lina Karout
Massachusetts General Hospital (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Roshan Fahimi
Massachusetts General Hospital (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Bernardo C. Bizzo
Massachusetts General Hospital (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Keith J Dreyer
Harvard University (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Mannudeep K. Kalra
Massachusetts General Hospital (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Subba R. Digumarthy
Harvard University (US)
1 shared publication
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
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