CAREER: Making Domain-Specific AI Models Steerable by Leveraging Foundational Models — NSF Award to Trustees of Boston University
Artificial intelligence (AI) tools are increasingly used in healthcare systems to help diagnose diseases from medical images such as Computerized Tomography (CT) scans and mammograms. While these systems can be highly accurate, they often learn unintended patterns, such as utilizing hospital-specific markings rather th
| Award title | CAREER: Making Domain-Specific AI Models Steerable by Leveraging Foundational Models |
|---|---|
| Award ID | 2443167 |
| Awardee | Trustees of Boston University |
| City | BOSTON |
| State | MA |
| Amount obligated | $499,997 |
| Principal investigator | Kayhan batmanghelich |
| Program | Smart and Connected Health |
| Start date | 09/01/2025 |
| Abstract | Artificial intelligence (AI) tools are increasingly used in healthcare systems to help diagnose diseases from medical images such as Computerized Tomography (CT) scans and mammograms. While these systems can be highly accurate, they often learn unintended patterns, such as utilizing hospital-specific markings rather than markers of disease. This can lead to uneven or unsafe performance. Compounding this problem, most AI models are “black boxes,” offering little insight into how decisions are mad |
| Source | NSF Awards |
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