Enhancing Large Language Models with Google Knowledge Integration for Rapid Response in Healthcare Emergencies

Abstract

We propose a novel framework that leverages knowledge from Google and utilizes Generative Pretrained Trans-former (GPT) to enhance the capabilities of GPT. The GPT’s knowledge and the responses generated from it are based on the data till 2021, it may generate incorrect responses for the questions that require knowledge after the year 2021. With the proposed architecture Large Language Models (LLMs) like GPT can be enhanced to produce responses without a knowledge barrier. Also, it is expected to provide much better answers as having access to Google as the knowledge regarding the specific domain would have been more informative. Emerging diseases and global health crises require rapid, precise, and adaptive responses. While advanced Artificial Intelligence (AI) systems, like GPT, can provide valuable support in many aspects of healthcare, they are not a substitute for the expertise, experience, and real-time decision-making abilities of healthcare professionals and epidemiologists. The response to novel and recent diseases often necessitates extensive collaboration among scientists, healthcare workers, and public health organizations, as well as access to specialized medical knowledge and resources. AI systems can assist in data analysis, information retrieval, and decision support, but ultimately, it is the collective human effort that drives effective responses to these complex challenges, especially when the situation is rapidly evolving or novel in nature.

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