Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations

Abstract

This narrative review presents a comprehensive and state-of-the-art synthesis of how machine learning (ML) is transforming public health through enhanced prediction, personalized treatment, real-time surveillance, and intelligent resource optimization. Drawing from 170 peer-reviewed studies published up to 2024/2025, this work uniquely integrates cross-domain insights spanning disease outbreak forecasting, genomic data analysis, personalized medicine, mental health monitoring, and public health infrastructure planning. The novelty of this review lies in its multidimensionality. It merges technical efficacy, ethical challenges, and future trends into a unified narrative. Our findings show substantial performance gains across domains: for example, ML models such as LightGBM, GRU neural networks, and LSTM achieved disease prediction accuracies ranging from 88 to 95%. In genomics, ML methods enabled nuanced disease subtype discovery and improved the accuracy of cancer risk assessment and pharmacogenomic modeling. Mental health prediction systems based on NLP and wearable data delivered up to 91% accuracy in stress and depression detection, while hospital resource forecasting models using deep learning minimized errors in predicting emergency admissions. Ethically, this review surfaces critical issues, including algorithmic bias, data privacy concerns in mental health analytics, and the interpretability of black-box models used in outbreak surveillance. A forward-looking discussion identifies future priorities such as the integration of multi-omics data, deployment of explainable AI, and equitable data inclusion frameworks. This review stands out by not only cataloguing applications but also offering a systems-level perspective on how ML can equitably and ethically scale to support public health strategies globally. It is among the first narrative reviews to concurrently evaluate ML’s predictive power, ethical constraints, and domain-specific improvements across all core pillars of public health.