Transforming Public Health Services with Explainable Artificial Intelligence: A Review across Interpretable Machine Learning, Deep Learning, Computer Science, Data Science, and Data Analytics
DOI:
https://doi.org/10.70445/gjus.3.1.2026.103-127Keywords:
Explainable Artificial Intelligence, Interpretable Machine Learning, Deep Learning, Public Health Services, Data Science, Data Analytics, Ethical AIAbstract
As artificial intelligence continues to revolutionize public health, Explainable Artificial Intelligence (XAI) is playing a pivotal role in promoting transparency, interpretability, and evidence-based decision-making. The review addresses the current status of the integration of interpretable machine learning, explainable deep learning, computer science, and data science and data analytics in public health applications. It examines how they are involved in predicting and monitoring outbreaks of disease, identifying risk, distributing health care resources, and managing populations' health. Issues of algorithmic bias, data privacy, model complexity, and ethical accountability are also discussed in the review. To build trustworthy AI systems that enhance public health outcomes and facilitate sustainable healthcare decisions, future studies should focus on reliable explanations, fairness, and privacy protection.
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