
The Harvey L. Neiman Health Policy Institute and the Georgia Institute of Technology (Georgia Tech) are teaming up to launch the Health Economics and Analytics Lab (HEAL), a new research center that will apply big-data analytics and artificial intelligence (AI) to large-scale medical claims databases.
The five-year, $3 million research partnership aims to better understand -- with a focus on medical imaging -- how evolving healthcare delivery and payment models affect patients and providers, according to the institute. HEAL, which will be part of Georgia Tech's Ivan Allen College of Liberal Arts, will be led by Danny Hughes, PhD, executive director of the Neiman Institute and a Georgia Tech professor of economics.
Neiman said the lab will support full-time postdoctoral researchers, graduate research assistants, and affiliated Georgia Tech faculty to produce both methodological and policy-oriented research. Secondarily, the lab will provide training and mentorship to radiologists interested in performing health economics and health policy research, according to the institute.
In addition to financial support, Neiman will also provide HEAL researchers with access to its data resources, including large-scale medical claims databases covering millions of U.S. residents.












![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






