AI and Public Health: TSA Pre Health Check
Jacob Lin, Habib Aina · Team Best Team
Submitted to AI Policy Hackathon at Johns Hopkins University. Sprint projects are early-stage work by participants, not Apart Research publications.
The TSA Pre Health Check introduces a proactive, AI-powered solution for real-time disease monitoring at transportation hubs, using machine learning to assess traveler health risks through anonymized surveys. This approach aims to detect and prevent outbreaks earlier, offering faster, targeted responses compared to traditional methods and potentially influencing future AI-driven public health policies.
Reviews
Very relevant problem and an elegant solution. To dig deeper I would research where the bottle necks in the system is and whether your solution is adressing one of these. I would also look into if the accuracy of vanilla OpenAI models are good enough or if you need to do a finetune or some other more specific solution.
The importance of real-time infectious disease monitoring can not be overstated. This project creates an avenue for travelers and airport authorities to catch and prevent likely outbreaks at points of transit. The mobile and web app is simple and intuitive.
Great idea and I can see this being helpful to prevent public health crisis. However, PDF says that the tool would be used for outbreak prediction & monitoring, but demo is an AI-driven survey tool. Using self-reported data may lead to inaccuracies. Would also benefit from thoughts/explanation about privacy when it comes to infectious disease tracking.
Cite this project
@misc{lin2024ai,
title = {{AI and Public Health: TSA Pre Health Check}},
author = {Jacob Lin and Habib Aina},
year = {2024},
month = oct,
note = {Submitted to AI Policy Hackathon at Johns Hopkins University, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/ai-and-public-health-tsa-pre-health-check}},
url = {https://apartresearch.com/sprints/projects/ai-and-public-health-tsa-pre-health-check}
}More from AI Policy Hackathon at Johns Hopkins University
- 1st place by peer reviewView project: Robust Machine Unlearning for Dangerous Capabilities
Robust Machine Unlearning for Dangerous Capabilities
Robust Machine Unlearning for Dangerous Capabilities
We test different unlearning methods to make models more robust against exploitation by malicious actors for the creation of bioweapons.
- View project: Infectious Disease Outbreak Prediction and Dashboard
Infectious Disease Outbreak Prediction and Dashboard
Infectious Diseace Dashboards
Our project developed an interactive dashboard to monitor, visualize, and analyze infectious disease outbreaks worldwide. It consolidates historical data from sources like WHO, OWID, and CDC for diseases including …
- View project: Modernizing DC’s Emergency Communications
Modernizing DC’s Emergency Communications
AI-CAD
The District of Columbia proposes implementing an AI-enabled Computer-Aided Dispatch (CAD) system to address critical deficiencies in our current emergency alert infrastructure. This policy establishes a framework for …