Artificial Intelligence against COVID-19: A Narrative Review
Abstract
The COVID-19 pandemic accelerated the development and deployment of artificial intelligence (AI) across clinical medicine, public health, biomedical research, and pharmaceutical development. This narrative review synthesizes representative evidence published primarily from 2024 to 2026, a period chosen to capture the post-emergency phase of COVID-19 in which external validation, multimodal modelling, post-COVID condition, genomic and wastewater surveillance, antiviral discovery, and responsible AI governance have become increasingly prominent. A structured narrative search of PubMed/PMC and World Health Organization (WHO) resources was conducted up to 25 September 2026 using combinations of COVID-19/SARS-CoV-2 and artificial intelligence/machine learning terms together with domain-specific keywords. Evidence was appraised qualitatively according to study population, data source, validation design, reported uncertainty, confounding, and demonstrated clinical or public-health relevance. Recent studies demonstrate that AI can support medical-image interpretation, risk stratification, population surveillance, long-COVID identification, variant characterization, and molecular screening. However, strong internal computational performance does not necessarily translate into external validity or clinical utility. Important 2024 studies showed that apparent diagnostic performance can decline substantially after confounding is controlled or when models are transferred across institutions and imaging devices. Multimodal models integrating electronic health records, patient-reported information, and genomics show modest gains in discrimination, but prospective clinical benefit remains to be established. The evidence indicates that future systems should emphasize external validation, calibration, explainability, privacy, fairness, continuous monitoring, and human clinical oversight. Rather than replacing laboratory testing, clinical judgment, epidemiology, or biomedical experimentation, AI is most appropriately positioned as an integrating and decision-support technology.