Harnessing artificial intelligence to address diseases attributable to unsafe drinking water: challenges, potentials, and recommendations

Unsafe drinking water is a global concern that poses serious risks to public health, especially in developing nations, where tainted water can spread diseases such as cholera, polio, and diarrhea, which can result in several health issues and deaths. Children and immunocompromised people are the most vulnerable groups that suffer disproportionately from waterborne illnesses. Promising approaches to reduce the burden of waterborne diseases and revolutionize drinking water management are provided by artificial intelligence (AI). Public health authorities and water industries can improve safe drinking water distribution, treatment, and monitoring using AI-powered models and approaches. AI enables predictive modeling to support sustainable water management techniques, maximize resource usage, and identify problems with infrastructure and water quality earlier. AI, coupled with Geographic Information Systems (GIS) and machine learning (ML) models such as random forest classifiers, aids in cholera risk prediction and enhances waterborne disease detection. Advanced AI models facilitate drought forecasting, reservoir optimization, and real-time water monitoring, improving water management and resource conservation. AI-driven systems, including predictive analytics and intelligent water distribution models, show potential for enhancing water safety, mitigating risks, and promoting sustainable water practices. However, several challenges must be overcome when incorporating AI into water management, such as concerns about data quality, infrastructure constraints, and ethical difficulties. Genetic sequencing and metagenomic analyses, which provide insights into microbial dynamics and water quality maintenance, are potential future areas in AI applications for water management. A balanced approach prioritizing equitable deployment, infrastructure readiness, workforce development, robust governance, collaborative efforts, ethical standards, and transparent regulatory frameworks, ensuring social equity and economic efficiency with current norms and policies, is required for AI integration to address diseases attributable to unsafe drinking water. These AI models are expedient to fully optimize WASH disease management to increase access to clean water, reduce the incidence of waterborne diseases, and advance global health.