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Introduction
Unsafe drinking water is a significant global issue, with profound implications for public health. Contaminated water can transmit diseases such as diarrhea, cholera, dysentery, typhoid, and polio, leading to the death of larger populations, especially in developing countries [1]. Chemical exposure to drinking water can result in various health effects, including gastrointestinal illnesses, nervous system disorders, and chronic conditions such as cancer [2]. Water contamination can occur at its source or during distribution, affecting vulnerable populations, such as infants, pregnant women, and individuals with weakened immune systems [2]. This underscores the critical need for global efforts to ensure access to clean and safe drinking water to prevent diseases, improve health outcomes, and empower communities worldwide [3].
Artificial intelligence (AI) is a set of computational technologies and algorithms that perform tasks typically requiring human intelligence, including predictive analytics, the Internet of Things (IoT), natural language processing (NLP), and geospatial technologies [4]. Predictive analytics uses statistical algorithms and machine learning (ML) techniques to forecast future events based on historical data, enabling water management to identify at-risk areas before problems arise [5]. IoT sensors in water systems can monitor water quality in real-time, detecting contaminants and alerting authorities [6]. NLP enables machines to understand and interpret human language, identifying trends and emerging issues in water quality reports and public health records [7]. Geospatial technologies, like Geographic Information Systems (GIS), analyze spatial data to understand water contamination’s geographical distribution and impact on communities [8]. AI has significant potential for addressing the burden of diseases related to unsafe drinking water. The integration of AI into tackling diseases caused by unsafe drinking water necessitates a multifaceted approach that combines predictive analytics, IoT, NLP, and geospatial technologies. This collaboration improves the ability to forecast, monitor, and minimise the risks associated with hazardous drinking water, resulting in better public health outcomes [9, 10].
AI-powered technologies can play a crucial role in various aspects of drinking water management and disease prevention [9]. Annually, approximately 505,000 deaths are attributed to diarrheal diseases caused by microbiologically contaminated drinking water [11]. The impact of unsafe drinking water extends beyond diarrheal diseases, affecting various aspects of global health and well-being. A lack of access to safe water sources is a significant risk factor for infectious diseases, malnutrition,...