Introduction: The application of AI in public health surveillance presents a transformative potential for more efficient work methodologies, offering new capabilities for the detection, notification, and response to public health threats. However, it's important to be aware of the associated risks and the ethical and legal challenges linked to AI's use in such a sensitive area as public health. The general objective of this study was to explore the application of AI in public health surveillance in Portugal, as viewed by Portuguese experts. Methods: The methodological approach employed was a qualitative study, utilising a descriptive and exploratory investigation. A content analysis was performed on 28 anonymised semi-structured interviews. This process identified concepts, themes, key ideas, and emergent patterns. The findings were then grouped into categories and subcategories, which allowed to highlight significant consensuses and meaningful insights. Results: Experts recognised AI’s transformative potential in enhancing predictive capacity, automating data processes, and supporting decision-making. However, they highlighted critical limitations of the Portuguese system, notably entrenched reactivity, fragmented infrastructures, under-utilisation of primary care and personal device data, and insufficient surveillance of non-communicable diseases. Key barriers include poor data quality and interoperability, absence of comprehensive data governance, shortage of AI-skilled professionals, resistance to organisational change and limited financial sustainability. Ethical concerns, privacy, algorithmic bias, and explainability, were emphasised as central to AI’s legitimacy. Comparisons with international experiences revealed that progress depends less on technological readiness than on systemic reform, data harmonisation, and strong governance aligned with European Union (EU) and World Health Organization (WHO) frameworks. Conclusion: AI can shift Portuguese public health surveillance from reactive to predictive, but only if supported by robust data ecosystems, clear governance, and investment in human capital. A national roadmap is required, prioritising interoperability, sustainable financing, and ethical implementation, to ensure that AI serves as a complement to, rather than a replacement for, human judgement in protecting population health.
Certo et al. (2026) studied this question.