1. How do you reconcile the paper’s extensive discussion of AI’s potential for early outbreak detection (Section 3) with the acknowledged failure of these systems during COVID-19? The paper cites ProMED’s manual detection of COVID-19 as a success story, but this fundamentally undermines your argument—it was not an AI system. No AI tool actually detected COVID-19 early in 2019. If these predictive systems were supposedly available, why did they fail when it mattered most? This is not merely a limitation but a catastrophic failure that should fundamentally challenge your thesis.
2. The paper repeatedly emphasizes AI’s ability to “optimize resource allocation” and “anticipate demand” (Sections 4.1, 4.2), yet the only real-world example provided, panic buying detection by a retail chain, operates in a commercial environment with stable infrastructure, vast historical data, and profit-driven optimization. How can you justify extrapolating this to public health emergencies where infrastructure collapses, historical data is absent, and equity (not efficiency) must be the primary concern? The 2017 earthquake example you cite (Section 4.1) is post-hoc simulation, not actual deployment during a crisis.
3. You cite a study finding AI-generated health messages “superior” to human-generated ones (Section 7), but this was based on folic acid messaging, a low-stakes, uncontroversial health topic. How can you ethically justify recommending AI-generated messaging for pandemic communication where misinformation kills, where trust is fragile, and where human empathy and cultural sensitivity are paramount? This seems dangerously naive about the sociopolitical realities of pandemic communication.