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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
Mannudeep K. Kalra
Massachusetts General Hospital (US)
21 shared publications
- Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience
- Revisiting the Trustworthiness of Saliency Methods in Radiology AI
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Overlooked Trustworthiness of Saliency Maps
Showing 5 of 21 shared publications
Bernardo C. Bizzo
Massachusetts General Hospital (US)
10 shared publications
- Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
Showing 5 of 10 shared publications
Keith J. Dreyer
Massachusetts General Hospital (US)
10 shared publications
- Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
Showing 5 of 10 shared publications
Parisa Kaviani
Massachusetts General Hospital (US)
9 shared publications
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
Showing 5 of 9 shared publications
Lina Karout
Massachusetts General Hospital (US)
9 shared publications
- Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Survey of CT radiation doses and iodinated contrast medium administration: an international multicentric study
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
- Low Contrast Volume Protocol in Routine Chest CT Amid the Global Contrast Shortage: A Single Institution Experience
Showing 5 of 9 shared publications
Subba R. Digumarthy
Harvard University (US)
9 shared publications
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Survey of CT radiation doses and iodinated contrast medium administration: an international multicentric study
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
Showing 5 of 9 shared publications
Pingkun Yan
Massachusetts General Hospital (US)
6 shared publications
- Revisiting the Trustworthiness of Saliency Methods in Radiology AI
- Overlooked Trustworthiness of Saliency Maps
- Quantifying Trustworthiness of Explainability in Medical AI
- Multi-view x-ray dissectography improves nodule detection
- X-ray Dissectography Improves Lung Nodule Detection
Showing 5 of 6 shared publications
Ge Wang
Rensselaer Polytechnic Institute (US)
5 shared publications
- Revisiting the Trustworthiness of Saliency Methods in Radiology AI
- Overlooked Trustworthiness of Saliency Maps
- Quantifying Trustworthiness of Explainability in Medical AI
- Multi-view x-ray dissectography improves nodule detection
- X-ray Dissectography Improves Lung Nodule Detection
Shadi Ebrahimian
Massachusetts General Hospital (US)
5 shared publications
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Survey of CT radiation doses and iodinated contrast medium administration: an international multicentric study
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
- Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm
Nir Neumark
Office of the General Counsel (US)
5 shared publications
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
- Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm
James Hillis
Office of the General Counsel (US)
4 shared publications
- Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience
- Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
- Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm
Emiliano Garza-Frias
Harvard University (US)
4 shared publications
- Survey of CT radiation doses and iodinated contrast medium administration: an international multicentric study
- Low Contrast Volume Protocol in Routine Chest CT Amid the Global Contrast Shortage: A Single Institution Experience
- No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models
- Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study
Salma Sadaf
Employees State Insurance Post Graduate Institute of Medical Sciences and Research (IN)
3 shared publications
- Radiation exposure in pregnancy: need for awareness
- Incidence of Third Trochanter in Human Femora and It’s Morphometry in Indian Population
- DRUG UTILIZATION PATTERN AND RISK ELEMENTS OF STROKE PATIENTS IN A TERTIARY CARE HOSPITAL: A CROSS SECTIONAL STUDY.
Reya V. Gupta
Massachusetts General Hospital (US)
3 shared publications
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
Hanqing Chao
Rensselaer Polytechnic Institute (US)
3 shared publications
- Revisiting the Trustworthiness of Saliency Methods in Radiology AI
- Overlooked Trustworthiness of Saliency Maps
- Quantifying Trustworthiness of Explainability in Medical AI
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