AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review

Area of Science:

• Computational biology
• Infectious disease informatics
• Machine learning applications

Background:

• Artificial intelligence (AI) techniques, including machine learning (ML), are increasingly vital in managing viral infections like influenza, RSV, HIV, and SARS-CoV-2.
• AI models offer advanced capabilities in diagnostics, real-time surveillance, and accelerating drug and vaccine development for viral diseases.

Purpose of the Study:

• To review the transformative impact of diverse AI techniques on the management of key viral respiratory and retroviral infections.
• To identify persistent challenges and propose recommendations for the responsible and equitable implementation of AI in viral outbreak responses.

Main Methods:

• Utilized a review of current AI applications, including gradient-boosted decision trees, support-vector machines (SVMs), deep neural networks (DNNs), and transformer architectures.
• Examined specific AI models like eXtreme Gradient Boosting (XGBoost), Random Forests, and convolutional neural networks (CNNs) for diagnostic and surveillance tasks.
• Analyzed AI's role in HIV research for viral-protein classification and drug-resistance mapping.

Main Results:

• AI has improved diagnostic accuracy for respiratory infections using symptom-based triage and imaging classifiers.
• Transformer models and social media surveillance provide real-time monitoring for diseases like COVID-19.
• AI accelerates antiviral and vaccine discovery by advancing viral-protein classification and drug-resistance mapping in HIV research.

Conclusions:

• AI significantly enhances viral disease management, diagnostics, and research, demonstrating broad applicability across different viruses.
• Key challenges include data heterogeneity, model interpretability, LLM hallucinations, and infrastructure limitations in low-resource settings.
• Standardized data pipelines and explainable AI (XAI) are crucial for safe, equitable deployment of AI interventions in future viral outbreaks.