Advancing Urban Health Assessment through Generative AI-Driven Indicators: GCR Case Study

Abstract

The nexus between urban environment and health has been firmly established by the research community. Despite recognition of the importance of well-being and health within the urban context, and the existence of over 145 indices related to health that have been developed within this realm. A noticeable gap exists in the Egyptian context, marked by the absence of an established urban health index framework. This issue underscores the pressing need for the development of a standardized cohesive set of urban health indicators tailored to the Egyptian urban environment. Furthermore, the fast-paced nature of contemporary life increased reliance on Artificial Intelligence (AI) technology. In this context, the present investigation delves into the employment of AI in the derivation of urban health indicators, aiming to comprehensively assess health within the urban context. The adopted methodology integrates AI-language tools, survey mechanisms, and statistics to formulate and validate an AI-derived index. This study aims to examine the reliability and validity of indicators developed by AI for assessing the health of urban environments. The study initially incorporated 78 indicators derived from four distinct AI-tools, which were then evaluated through an online survey targeting urban experts. This evaluative process culminated in the identification of 34 indicators that exhibited robustness and aptitude for inclusion in the refined index. The study additionally demonstrated that agreement with AI-derived index varied depending on gender, professional role, and years of experience. This study underscores potential for AI-driven methodologies to inform urban planners, policymakers, and decision-makers about creation of healthier, more sustainable, and resilient cities.