Match tier
Listed
Presence
Current · Arkansas
Last published
2024
Sources
OpenAlex · ORCID
Refreshed
2026-08-08
J
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Jacob Brecheisen's research focuses on the intersection of artificial intelligence, medical imaging, and diagnostic interpretation. His work investigates the development of controllable and interpretable AI systems designed to assist radiologists in their diagnostic processes, particularly for chest X-ray (CXR) analyses. Brecheisen explores methods for modeling radiologists' intentions and decoding their focus to enhance the accuracy of AI-assisted diagnoses. His recent publications highlight the creation of AI systems, such as 'I-AI' and 'ItpCtrl-AI,' which aim to provide transparency and control in AI's application within radiology. He has collaborated with researchers Trong Thang Pham and Ngan Le at the University of Arkansas at Fayetteville on shared publications.
Metrics
- h-index: 2
- Publications: 3
- Citations: 21
Selected Publications
-
ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions (2024)
-
I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses (2024)
Collaboration Network
Top Collaborators
Trong Thang Pham
University of Arkansas at Fayetteville
1 shared publication
In database
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
Anh Phong Nguyen
University of Liverpool (GB)
1 shared publication
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
Hien Nguyen
University of Houston (US)
1 shared publication
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
Ngan Le
University of Arkansas at Fayetteville
1 shared publication
In database
- I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses
Trong-Thang Pham
University of Arkansas at Fayetteville (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Carol C. Wu
The University of Texas MD Anderson Cancer Center (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Hien Van Nguyen
University of Houston (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Zhigang Deng
University of Houston (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Donald Adjeroh
Donaldson (United States) (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Gianfranco Doretto
West Virginia University Hospitals (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Arabinda K. Choudhary
University of Arkansas for Medical Sciences (US)
1 shared publication
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
Ngan Le
University of Arkansas at Little Rock
1 shared publication
In database
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
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