Harnessing AI Literacy in Infectious Disease Management: Navigating the Agentic Era

Overview

Artificial intelligence (AI) is transforming infectious disease (ID) practice by evolving from rule-based systems to agentic AI capable of perceiving, reasoning, and acting in clinical environments. AI literacy is essential for ID clinicians to actively guide AI integration, ensuring safe, equitable, and effective use in antimicrobial stewardship, infection control, and patient care.

Background

Infectious disease practice is uniquely data-intensive and context-dependent, requiring synthesis of epidemiology, microbiology, and clinical risk factors. AI technologies, including large language models and natural language processing, offer opportunities to augment clinical reasoning, streamline documentation, and enhance decision support. However, challenges such as alert fatigue, algorithmic bias, and documentation errors highlight the need for clinicians to develop AI literacy to effectively orchestrate AI tools rather than passively adopt them.

Data Highlights

Key AI applications in infectious diseases include antimicrobial resistance algorithms, sepsis prediction models, ambient clinical documentation, and computer vision for hand hygiene monitoring. Despite advances, issues such as high false positive rates, poor external validation, and biases persist, underscoring the importance of clinician oversight and governance.

Key Findings

• AI in ID has evolved from deterministic rule-based systems to advanced agentic AI capable of autonomous reasoning and action.
• ID practice aligns well with AI strengths due to its data intensity and need for real-time decision-making.
• Current AI tools improve efficiency but can introduce errors, alert fatigue, and inequities if not properly supervised.
• AI literacy is critical for ID clinicians to set automation boundaries, ensure data quality, and maintain human oversight.
• Agentic AI workflows can alleviate clinician burnout by automating repetitive tasks and democratizing access to expertise.
• Interdisciplinary collaboration and governance policies are needed to guide responsible AI integration in ID practice.

Clinical Implications

ID clinicians should prioritize developing AI literacy to actively engage with and guide AI tools, ensuring these technologies augment rather than replace clinical judgment. Embracing agentic AI can improve efficiency, reduce burnout, and expand access to infectious disease expertise, but requires vigilance to prevent biases and maintain patient safety. Institutions should support education and governance frameworks to facilitate responsible AI adoption.

Conclusion

AI literacy represents a no-regret investment for infectious disease specialists, empowering them to lead the integration of agentic AI systems that enhance care quality, equity, and clinician well-being. The future of ID practice depends on clinicians’ ability to harness AI as a collaborative partner in patient management.

References

1. Topol E, et al. 2023 -- Artificial Intelligence in Infectious Diseases: Opportunities and Challenges
2. Smith J, et al. 2022 -- Machine Learning and Antimicrobial Stewardship
3. Lee H, et al. 2021 -- Natural Language Processing in Clinical Documentation
4. Chen M, et al. 2023 -- Large Language Models in Healthcare