Using AI to improve assessments of environmental health studies
Official title: Accelerating Risk-of-Bias (RoB) Assessment in Environmental Health Studies Using Large Language Models
Funded at Pico Portal, INC. · NIH-11184662 · Data updated 2026-11-12
This study is working on smart computer tools to help quickly and accurately check for bias in research about harmful substances like PFAS, so that patients and communities can get better and more reliable information about environmental health.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Pico Portal, INC. NIH-funded |
| Lab location | 1 site (Saint Petersburg, United States) |
| Project start | June 2, 2025 |
| Project end | May 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $306,872 |
| NIH project number | 1R43ES037589-01 |
| Project ID | NIH-11184662 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals affected by environmental health issues, particularly those exposed to per- and polyfluoroalkyl substances (PFAS).
✕Less likely to help
Patients who are not impacted by environmental health risks or who do not have exposure to PFAS may not receive direct benefits from this research.
What this research studies
This research focuses on developing advanced AI tools to automate and enhance the efficiency of Risk-of-Bias assessments in environmental health systematic reviews. By leveraging large language models, the project aims to streamline the process of identifying and extracting critical information from studies related to harmful substances like PFAS. The goal is to create a system that can perform these tasks with accuracy comparable to human experts, ultimately making the review process faster and less costly. Patients and communities can benefit from more reliable environmental health data that informs better health policies and practices.
Why it matters
Potential benefit: If successful, this research could lead to more efficient and accurate assessments of environmental health risks, ultimately improving public health outcomes.
How similar studies have gone: Other research has shown promise in using AI for data extraction and analysis in various fields, suggesting that this approach could be effective in environmental health as well.
Where this research is happening
-
Pico Portal, INC.
Saint Petersburg, United States Active
Researchers
- Principal investigator: Agai, Eitan — Pico Portal, INC.
- Study coordinator: Agai, Eitan
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.