Comprehensive Summary
This article highlights AI’s potential to strengthen public health surveillance by analyzing large, heterogeneous datasets such as hospital records, laboratory results, and social media. AI can detect unusual patterns faster than traditional systems, supporting early outbreak response and better resource allocation. However, the authors caution about risks including algorithmic bias, privacy issues, and lack of transparency. They argue that oversight and international cooperation are essential to ensure reliability and clinical viability.
Outcomes and Implications
AI has the potential to improve early disease detection by synthesizing medical records, test results, and genetic data. Earlier and more precise diagnoses can lead to targeted treatments, improved outcomes, and reduced outbreak risk. However, reliance on algorithms without sufficient validation introduces risks, as errors or data gaps may mislead care. Used responsibly, AI could become an essential tool for both patient care and public health preparedness.