Biography and Research Information
OverviewAI-generated summary
Mahima Saini's research focuses on health disparities, particularly in the context of colorectal cancer and persistent poverty areas within Arkansas. She has investigated the coverage of the Arkansas All-Payer Claims Database for examining these disparities, contributing to a better understanding of how such databases can be utilized for health equity research. Her work also extends to the regulatory landscape of artificial intelligence and machine learning in cardiovascular devices, exploring the challenges and opportunities presented by these emerging technologies in the U.S. Food and Drug Administration approval process. Saini collaborates with researchers at the University of Arkansas for Medical Sciences and the University of Arkansas at Fayetteville, including Jonathan Laryea and Mario Schootman, on shared publications. Her scholarship metrics include an h-index of 2 and four total publications.
Metrics
- h-index: 2
- Publications: 4
- Citations: 8
Selected Publications
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Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices (2025)
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Colorectal cancer survival disparities in persistent poverty areas (2025)
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Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients (2024)
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Sampling coverage of the Arkansas all‐payer claims database by County's persistent poverty designation (2024)
Collaboration Network
Top Collaborators
- Sampling coverage of the Arkansas all‐payer claims database by County's persistent poverty designation
- Colorectal cancer survival disparities in persistent poverty areas
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Sampling coverage of the Arkansas all‐payer claims database by County's persistent poverty designation
- Colorectal cancer survival disparities in persistent poverty areas
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Sampling coverage of the Arkansas all‐payer claims database by County's persistent poverty designation
- Colorectal cancer survival disparities in persistent poverty areas
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Sampling coverage of the Arkansas all‐payer claims database by County's persistent poverty designation
- Colorectal cancer survival disparities in persistent poverty areas
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Colorectal cancer survival disparities in persistent poverty areas
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients
- Colorectal cancer survival disparities in persistent poverty areas
- Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices
- Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices
- Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices
- Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices
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