Using artificial intelligence to improve public health: a narrative review

Abstract

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via 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 is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

Artificial intelligence for the optimal management of community-acquired pneumonia

“…Revolutionary methods for identifying individuals with an elevated risk of severe CAP are imperative. The realm of AI in healthcare is undergoing rapid advancements, with ongoing research delving into innovative approaches to enhance the efficacy of AI-driven disease risk scores specifically tailored for CAP [9 ▪ ,10 ▪ ,11 ▪▪ ]. Limitations of existing severity scores, such as PSI and CURB-65, particularly in predicting outcomes for critically ill patients requiring ICU admission highlight new approaches [4 ▪▪ ,24,34].…”

“…The increasing demand on healthcare systems necessitates more efficient and reliable risk assessment tools. The integration of artificial intelligence (AI) into healthcare has shown promise [9 ▪ ,10 ▪ ], particularly during the coronavirus disease 2019 (COVID-19) pandemic, accelerating innovation in AI-assisted medical care, especially in lung imaging and respiratory sounds. Machine learning (ML) technologies, such as deep learning algorithms, automate pneumonia diagnosis using various imaging modalities [11 ▪▪ ].…”

Applied machine learning techniques for chronic disease treatment default prediction and its potential benefits for patient outcome: A case series study approach

“…Some of the benefits as determined in a related study show how data-driven insights and informed decision-making with machine learning use has revolutionized healthcare and public health decision support systems. In public health delivery, predictive technique use has been beneficial in spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/ epidemic modeling and health diagnosis [9]. Other application areas for predictive technique use as identified include precision medicine, diagnosis and treatment recommendations, patient engagement and adherence and administrative activities [10].…”

Artificial Intelligence and Precision Public Health: A Balancing Act of Scientific Accuracy, Social Responsibility, and Community Engagement

“…A parallel transition is underway in public health field, with AI helping shape an era of precision public health, a concept with different definitions but broadly characterized by "delivering the right intervention at the right time, every time to the right population" [6][7][8][9][10]. Specifically, the intersection of AI and genomics impacts different public health activities, including the identification of disease risk factors, conducting disease surveillance, modelling, and forecasting, and developing data-driven public health policy [11]. Other prominent applications of AI and genomics in the public health area include detecting and understanding emerging public health threats (including healthcareassociated infections and foodborne illness), identifying novel virus variants, and enhancing the resolution of epidemiological investigation and surveillance for bacterial antimicrobial resistance [11,12].…”

“…Specifically, the intersection of AI and genomics impacts different public health activities, including the identification of disease risk factors, conducting disease surveillance, modelling, and forecasting, and developing data-driven public health policy [11]. Other prominent applications of AI and genomics in the public health area include detecting and understanding emerging public health threats (including healthcareassociated infections and foodborne illness), identifying novel virus variants, and enhancing the resolution of epidemiological investigation and surveillance for bacterial antimicrobial resistance [11,12]. Furthermore, AI can drive applications related to health promotion, behavioral changes, and the adoption of healthier lifestyles [2,13].…”