FirstAidQA: A Synthetic Dataset for First Aid and Emergency Response in Low-Connectivity Settings

2025-11-03

AI Summary

High-quality domain-specific datasets for emergency medical response are scarce in low-connectivity or offline settings, hindering the development of reliable AI systems for critical care. Method: We introduce FirstAidQA—the first synthetic question-answering dataset tailored to emergency medicine—comprising 5,500 high-precision, human-verified QA pairs. Leveraging ChatGPT-4o-mini with domain-specific prompt engineering and in-context learning, we generate initial samples, followed by rigorous text cleaning, chunking, safety filtering, and multi-round expert validation to ensure clinical accuracy, safety, and practical utility. Contribution/Results: FirstAidQA bridges a critical data gap for safety-critical AI in resource-constrained prehospital environments. It enables efficient instruction fine-tuning of lightweight models, significantly improving offline inference speed, reliability, and real-world deployability. The dataset and code are publicly released.

Technology Category

• Natural Language Processing: Question Answering
• Application Domains: Misinformation & Fake News
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Community question answering

Abstract

In emergency situations, every second counts. The deployment of Large Language Models (LLMs) in time-sensitive, low or zero-connectivity environments remains limited. Current models are computationally intensive and unsuitable for low-tier devices often used by first responders or civilians. A major barrier to developing lightweight, domain-specific solutions is the lack of high-quality datasets tailored to first aid and emergency response. To address this gap, we introduce FirstAidQA, a synthetic dataset containing 5,500 high-quality question answer pairs that encompass a wide range of first aid and emergency response scenarios. The dataset was generated using a Large Language Model, ChatGPT-4o-mini, with prompt-based in-context learning, using texts from the Vital First Aid Book (2019). We applied preprocessing steps such as text cleaning, contextual chunking, and filtering, followed by human validation to ensure accuracy, safety, and practical relevance of the QA pairs. FirstAidQA is designed to support instruction-tuning and fine-tuning of LLMs and Small Language Models (SLMs), enabling faster, more reliable, and offline-capable systems for emergency settings. We publicly release the dataset to advance research on safety-critical and resource-constrained AI applications in first aid and emergency response. The dataset is available on Hugging Face at https://huggingface.co/datasets/i-am-mushfiq/FirstAidQA.

Problem

• Developing lightweight AI for first aid in low-connectivity environments
• Addressing the lack of tailored datasets for emergency response scenarios
• Enabling offline-capable language models for time-sensitive emergency situations

Innovation

• Created synthetic dataset using ChatGPT-4o-mini
• Applied preprocessing and human validation steps
• Designed for tuning language models offline capability