Challenges, opportunities, and future directions in AI for COVID-19 response

Abstract

The COVID-19 pandemic highlighted critical limitations of artificial intelligence (AI) in healthcare sectors and in public health response. This chapter proposes a short review of how the developed AI technologies improved during the pandemic across different domains such as diagnostics, drug discovery, healthcare logistics, and public communication. It highlights AI’s critical role in augmenting clinical decision-making using different AI tools applied to medical imaging, predictive analysis of the virus spread, and bioinformatics tools for genomic analysis. The chapter also presents different methodological advancements in machine learning (ML), deep learning (DL), computer vision, large language models (LLMs), and explainable AI techniques. The COVID-19 pandemic demonstrated the great promise and sobering limitations of AI for public health response. This section provides an extensive summary of how AI applications have been used during the pandemic in settings ranging from diagnostics, epidemiological modeling, drug discovery, and healthcare logistics, to public communication. It emphasizes ‘the importance of AI in enhancing clinical decisions using DL models engineered on radiological imaging, predictive modeling for disease spread, and bioinformatics tool for genomic analysis.’ chapter is also concluded with a large synthesis of recent state-of-the-art in terms of methodology, including classical ML, computer vision, LLM and explainable AI techniques. However, AI was not yet extensively implemented under the deficiencies of data heterogeneity, limited generalizability, privacy infringement and the absence of ethical and legal underpinnings. The work shows that a lot of AI was trained on very limited datasets, which raises questions about bias and fairness, particularly in underprivileged populations. In looking ahead, the chapter highlights four strategic pathways to support the efficacy of AI in succeeding pandemics—integration of AI into pandemic preparedness through federated and privacy-preserving learning; the development of explainable and trustworthy AI models to promote clinician adoption; the commitment to global equity in AI accessibility, particularly in low-resource environments; and the development of governance frameworks that are capable of balancing innovation with ethical stewardship. These observations, in combination, underscore the importance of interdisciplinary collaboration, infrastructural investment, and regulatory harmonization in guaranteeing responsible and equitable AI use during global health crises.

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