AI in Public Health: Trends, Challenges, and Future Directions

Artificial intelligence (AI) is rapidly transforming healthcare, particularly in medicine and healthcare administration. Its recent expansion into public health, accelerated by the COVID-19 pandemic, highlights its potential to revolutionize disease management, prevention, and overall public health strategies. This article explores the advancements of AI in public health, addressing both its benefits and the challenges that lie ahead. AI applications include spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health faces hurdles such as limited infrastructure, technical understanding, data scarcity, and ethical concerns.

This review employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach to comprehensively explore AI's application in enhancing public health. Relevant electronic databases such as PubMed, Scopus, and Web of Science were searched using keywords related to AI and public health. Studies focusing on AI applications in public health, published in English, were included. Studies unrelated to public health, published in other languages, or lacking full-text availability were excluded. The findings were synthesized in a narrative format, identifying key themes, trends, and patterns related to AI's role in improving public health practices. The implications of these findings for public health practice are also discussed.

The journey of AI in public health began in the 1960s with the aim of creating systems that mimic human intelligence. Early applications focused on expert systems for medical diagnosis and treatment planning. By the 1980s and 1990s, research expanded to machine learning and natural language processing. The availability of large medical databases and advanced computing systems allowed researchers to explore AI's potential in medical diagnosis, drug discovery, and public health surveillance. The 2000s saw advancements in computer vision, natural language processing, and machine learning, leading to more complex AI systems capable of analyzing large datasets and predicting outcomes. This era introduced AI-based diagnostic systems for analyzing medical images and identifying diseases like cancer. The COVID-19 pandemic further accelerated the use of AI in public health, particularly in predicting disease spread, contact tracing, and rapid testing. Despite these advancements, ethical and legal concerns regarding data privacy, security, transparency, and algorithmic bias must be addressed to ensure responsible integration of AI into healthcare systems.

Predictive modeling combines statistical models and machine learning techniques to forecast future outcomes. In public health, it is used to predict the spread of infectious diseases like COVID-19 and influenza. By analyzing data on past epidemics, population demographics, and climate patterns, predictive models can identify patterns and trends that inform public health measures. This capability enhances our ability to forecast disease spread and guide public health treatments. The central challenge addressed by AI in predictive modeling is improving the accuracy, efficiency, and actionable insights in public health decision-making. Traditional methods often struggle with data complexity, pattern identification, and accurate predictions. AI offers a transformative solution by addressing these challenges and achieving more effective outcomes. A clear categorization and summary of traditional and AI methods for predictive modeling, including disease forecasting, risk prediction, and spatial modeling, is crucial for informed decision-making, efficiency, accessibility, and research collaboration in public health.

Disease forecasting is a critical application of AI in public health, enhancing our ability to anticipate the spread of infectious diseases and inform public health measures. Historically, time series analysis and statistical techniques were used. However, AI enables the use of more complex algorithms and the evaluation of diverse data for more accurate predictions. Machine learning algorithms analyze various data sources, including social media and electronic health records, to identify patterns and forecast disease spread. The increasing accessibility of large datasets and advanced computing resources further enhances AI's predictive capabilities. AI can evaluate vast data volumes, identify patterns and trends, and estimate future outcomes, guiding public health initiatives and reducing the spread of infectious diseases. Despite these benefits, challenges remain, including data quality and integrity, and ethical and legal considerations regarding data security and privacy. Future developments include integrating AI with IoT and wearable devices for real-time data and personalized disease forecasting using electronic health records. The core problem in disease forecasting is accurately predicting the future spread and impact of diseases. AI addresses this by efficiently analyzing large datasets, identifying hidden relationships, and detecting intricate trends, providing early alerts and actionable strategies to mitigate disease outbreaks. For example, Google AI has developed a model that can predict the number of COVID-19 cases in a region up to two weeks in advance.

Risk prediction is crucial for targeted disease prevention and management. Traditional risk prediction techniques can be time-consuming and unreliable. AI enhances the effectiveness and accuracy of risk predictions, leading to better public health outcomes. Machine learning algorithms analyze large datasets, such as electronic health records, to identify patterns and predict disease likelihood. These algorithms can examine intricate data, including genomics and medical imaging, to assess disease probability. The integration of AI with technologies like wearable devices and genomics has the potential to offer more precise predictions by providing accurate, real-time data. Explainable AI (XAI) tools can increase accountability and transparency, promoting trust in AI's use in healthcare. The central problem in risk prediction is identifying individuals at high risk of developing specific diseases. AI methods improve risk prediction by integrating diverse data sources, detecting non-linear relationships, and identifying latent patterns. The goal is to tailor interventions, allocate resources, and improve personalized healthcare strategies. For example, IBM Watson Health has developed a model that can predict the risk of heart attack with a high degree of accuracy.

Spatial modeling, the analysis of geographic information to recognize patterns and trends in health outcomes, is essential for locating interventions in areas with the highest disease burden. Traditional spatial modeling techniques can be time-consuming and may not always produce accurate results. AI can increase the effectiveness and precision of geographic modeling, improving public health outcomes. Machine learning algorithms analyze large-scale geographic data, such as satellite images, to find trends and forecast disease spread. For example, these techniques have been used to forecast the risk of dengue.

Despite the numerous benefits, the integration of AI in public health presents several challenges. These include ensuring data privacy and security, addressing algorithmic bias, and maintaining transparency in AI decision-making processes. Ethical considerations are paramount to prevent discrimination and ensure equitable access to AI-driven healthcare solutions. Robust regulatory frameworks are needed to govern the use of AI in public health, promoting responsible innovation and safeguarding public trust.

The future of AI in public health is promising, with potential advancements in personalized medicine, predictive analytics, and real-time disease monitoring. The integration of AI with other technologies, such as IoT devices and blockchain, can further enhance data collection, security, and interoperability. Continued research and development are essential to unlock the full potential of AI in improving public health outcomes globally.

AI is poised to revolutionize public health by enhancing disease management, prevention, and overall public health strategies. While challenges and ethical considerations must be addressed, the potential benefits of AI in improving public health outcomes are significant. By embracing responsible innovation and fostering collaboration among researchers, healthcare professionals, and policymakers, AI can be effectively leveraged to create a healthier and more equitable future for all.