Artificial intelligence and infectious diseases: an evidence-driven conceptual framework for research, public health, and clinical practice

Comprehensive Summary

Odone et al provides a conceptual overview of how Artificial Intelligence can be used in infectious disease managament and prevention. The authors proposed a conceptual framework by systematically mapping existing AI models and datasets relevant to infectious agents and susceptible hosts. Their detailed findings include categorization of domains where AI is already applied (e.g. outbreak detection, diagnostics, prediction of antimicrobial resistance) and proposals for bridging gaps in data availability and transparency. However, for real implementation in public health, the authors note that this project will require further multidisciplinary collaboration, governance structures, and thoughtful validation of models.

Outcomes and Implications

This research is important because infectious diseases remain a major global health threat, and AI offers potential in improving prevention and management. Clinically, the work suggests that AI could assist in early outbreak warnings, improve diagnostic accuracy and support decision-making, although the authors caution that translation to practice is not immediate. They expect incremental integration first in supportive roles over the next few years, but broader clinical implementation will likely depend on overcoming regulatory hurdles.