As climate change escalates and significant disasters arise more frequently, Taiwan's public safety concerns continue to grow. The gas explosion at the department store in Taichung in 2025 underscored the critical issue that the "golden rescue time" prior to the arrival of trained rescuers is frequently underutilized. During the second "2025 AI Wave: Taiwan Generative AI Applications Hackathon," the "Revenge Seekers" team identified a significant social issue and provided a unique AI-driven solution, ultimately securing victory in the "Resilient Future" category of Chunghwa Telecom.
"Revenge Seekers" wins Chunghwa Telecom's Resilient Future award with an AI-powered emergency assistant that helps save lives in disaster zones. Credit: Company
In response to the frequent occurrence of significant disasters in recent years, the "Revenge Seekers" team has developed an intelligent system that is designed to aid non-medical individuals during emergencies. The purpose of this system is to improve the disaster response capabilities of the entire population by leveraging technological advancements. The team conducted a comprehensive analysis of significant accidents, including the 2015 New Taipei water park dust explosion and the department store gas explosion in Taichung earlier this year, and identified three urgent issues at the disaster sites that required immediate attention.
First, the general public lacks basic first aid knowledge, preventing them from effectively using the "golden rescue time" before medical personnel arrive. Secondly, the inability to record and classify casualty information on-site in real time leads to the inefficient deployment of following medical resources. Thirdly, the prompt distribution of casualties is challenging to understand, significantly affecting the overall decision-making and resource allocation of governmental agencies during rescue operations. These problems become even more severe in mass casualty events.
The team developed "First Aid Master - Public Edition v2.0" to resolve the aforementioned pain points. This first aid assistance system integrates generative AI technology. It is capable of assisting individuals without a medical background in conducting preliminary triage of the injured and providing situational basic first aid guidance through an intuitive interface. The system will categorize the injured into four categories based on the current condition of the injured as entered by the user: red (immediate treatment), yellow (treatment can be postponed), green (minor injuries), and black (extremely low chance of survival). It will then provide corresponding first aid suggestions for each category in real time, such as stopping bleeding, maintaining airway patency, and performing CPR.
Furthermore, the rescue efficiency and resource allocation accuracy can be significantly enhanced by the system's ability to autonomously generate injury records and zoning information for the reference of professional medical teams that arrive later.
"First Aid Master" is built on Amazon Web Services (AWS) cloud infrastructure and leverages multiple innovative technologies to enhance the stability and responsiveness of system operations. The team utilized AWS PartyRock, a no-code, generative AI application builder, in conjunction with large language models such as Amazon Nova Pro and Anthropic Claude 3.5 Sonnet v2 as the primary AI engine. To enhance the professionalism and precision of answers, the team independently structured the emergency documents and developed a compact retrieval-augmented generation (RAG) system.
Amazon CloudFront delivers content with low latency through its content delivery network with secure HTTPS connections; Amazon Route 53 ensures reliable and efficient DNS routing, while Amazon Elastic Compute Cloud (Amazon EC2) provides scalable compute capacity for the website and backend services. The open-source search engine OpenSearch retrieves first aid knowledge base information, and AWS WAF helps protect web applications and APIs from attacks. The system incorporates an AED map feature to help individuals quickly locate the nearest automatic external defibrillator, significantly improving rescue response time—critical for patients experiencing cardiac arrest.
"Revenge Seekers" distinguished itself within the Chunghwa Telecom "Resilient Future" group. The judges determined that this technology has the potential to provide immediate assistance at disaster sites and to function as an emergency education instrument during peacetime to disseminate first aid knowledge to the public. The system has substantial practical utility and can help Taiwan establish a robust civilian emergency network. Moreover, the system's real-time summary of patient information provides public departments with valuable insights for decision-making, which are expected to enhance the allocation of medical resources.
Article edited by Sherri Wang
