Abstract & Details
Description
Award ID: 2330769
The broader impact/commercial potential of this I-Corps project is to revolutionize the current approach to decision-making processes in disease treatment. The project will help transform how patients, their families, and healthcare providers navigate the complex landscape of disease treatment options. It aspires to empower these stakeholders with personalized, data-driven treatment recommendations, considering a comprehensive set of individual patient attributes and real-world variables such as the location, budget, and insurance details. By making disease treatment more personalized and efficient, the project is poised to significantly improve treatment outcomes and reduce healthcare costs. Furthermore, its commercial potential is substantial, given the growing need for personalized healthcare solutions and the rise of artificial intelligence-powered applications in healthcare. This I-Corps project is based on the development of artificial intelligence-powered platform that provides personalized disease treatment recommendations. The innovation lies in leveraging advanced machine learning algorithms to analyze a wide array of patient-specific and disease-specific data, leading to more accurate treatment suggestions tailored to each individual patient's unique circumstances and features. The project aims to address the problem of overwhelming and often confusing treatment options by providing clear, data-backed suggestions via the developed platform. By doing so, it offers an opportunity to enhance the treatment decision-making process and ultimately improve the quality of patient care. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Molly Wasko
The broader impact/commercial potential of this I-Corps project is to revolutionize the current approach to decision-making processes in disease treatment. The project will help transform how patients, their families, and healthcare providers navigate the complex landscape of disease treatment options. It aspires to empower these stakeholders with personalized, data-driven treatment recommendations, considering a comprehensive set of individual patient attributes and real-world variables such as the location, budget, and insurance details. By making disease treatment more personalized and efficient, the project is poised to significantly improve treatment outcomes and reduce healthcare costs. Furthermore, its commercial potential is substantial, given the growing need for personalized healthcare solutions and the rise of artificial intelligence-powered applications in healthcare. This I-Corps project is based on the development of artificial intelligence-powered platform that provides personalized disease treatment recommendations. The innovation lies in leveraging advanced machine learning algorithms to analyze a wide array of patient-specific and disease-specific data, leading to more accurate treatment suggestions tailored to each individual patient's unique circumstances and features. The project aims to address the problem of overwhelming and often confusing treatment options by providing clear, data-backed suggestions via the developed platform. By doing so, it offers an opportunity to enhance the treatment decision-making process and ultimately improve the quality of patient care. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Molly Wasko
| Status | Closed |
|---|---|
| Effective start/end date | 06/01/23 → 12/31/24 |
Funding
- I-Corps Teams: $50,000.00
Active Fiscal Year
- FY2024
- FY2023
- FY2025
Start Fiscal Year
- FY2023
TIP Programs
- I-Corps Teams
Key Technology Areas
- Artificial Intelligence
- (confidence score: 100%)
- Biotechnology
- (confidence score: 99%)
Technology Foci
- Genomics and bioinformatics
- (confidence score: 99%)
- Machine Learning (ML)
- (confidence score: 98%)
Congressional District at Award
- District n. 08 of Missouri
Current Congressional District
- District n. 08 of Missouri
United States
- Missouri
Core Based Statistical Area (CBSA)
- Rolla, MO
County
- County: Phelps, MO
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