Modern public health surveillance is undergoing a transformative shift, driven by digital health technologies and artificial intelligence (AI). Traditional surveillance systems, which rely heavily on manual reporting and delayed data aggregation, often struggle to provide the real-time insights necessary for timely interventions. The integration of digital tools, including electronic health records, mobile health applications, wearable devices, and Internet of Things (IoT) platforms, has enabled the continuous collection of large-scale data across diverse populations. These technologies facilitate the early detection of disease outbreaks, improve the monitoring of chronic conditions, and enhance population health management. Artificial intelligence, including machine learning, natural language processing, and predictive analytics, further enhances the capabilities of digital surveillance systems by enabling automated data processing, pattern recognition, and forecasting. AI-driven models can analyze vast datasets from heterogeneous sources, including social media, environmental sensors, and clinical databases, to identify emerging health threats and predict disease trends. This capability has proven particularly valuable during global health crises, where rapid response and adaptive strategies are crucial. Artificial intelligence, including machine learning, natural language processing, and predictive analytics, further enhances the capabilities of digital surveillance systems by enabling automated data processing, pattern recognition, and forecasting. AI-driven models can analyze vast datasets from heterogeneous sources, including social media, environmental sensors, and clinical databases, to identify emerging health threats and predict disease trends. This capability has proven particularly valuable during global health crises, where rapid response and adaptive strategies are crucial. Despite these advancements, several challenges persist, including concerns related to data privacy, ethical considerations, interoperability, and algorithmic bias. The digital divide also limits equitable access to these technologies, potentially exacerbating existing health inequities. Furthermore, the reliability and validity of AI models depend on the quality and representativeness of the data used for training the models. This narrative review explores the evolving roles of digital health technologies and AI in modern public health surveillance. It examines key technological innovations, applications in disease monitoring and outbreak prediction, integration challenges, and ethical implications. By synthesizing the current evidence, this review highlights both the opportunities and limitations of these technologies and provides insights into future directions for building resilient, equitable, and data-driven public health surveillance systems.
Khobragade et al. (Wed,) studied this question.