Agentic AI systems that perceive, reason, and act within clinical environments to augment infectious disease practice
Target Population
Infectious disease clinicians and patients affected by infectious diseases
Care Setting
Diverse healthcare settings including tertiary health systems, rural hospitals, resource-constrained clinics, and low- and middle-income countries
Key Highlights
Agentic AI advances enable real-time synthesis of epidemiology, microbiology, resistance patterns, and clinical risk factors to support ID decisions.
AI literacy is essential for ID clinicians to actively guide AI tool use, ensuring appropriate automation boundaries, equity, and safety.
AI-powered workflows can reduce clinician burden by automating repetitive tasks and democratize access to ID expertise across varied care settings.
Guideline-Based Recommendations
Diagnosis
Incorporate AI tools such as antimicrobial resistance algorithms and sepsis predictive models to enhance diagnostic accuracy.
Use AI systems that integrate structured and unstructured data including clinical notes and microbiology reports.
Management
Adopt agentic AI workflows to augment antimicrobial stewardship and infection control programs.
Ensure human oversight in critical decisions to mitigate risks of AI errors and biases.
Advocate for high-quality data inputs and continuous validation of AI tools in clinical practice.
Monitoring & Follow-up
Monitor AI tool performance for false positives, generalizability, and equity across diverse patient populations.
Establish governance policies and interdisciplinary collaboration to oversee AI integration and impact.
Risks
Be aware of alert fatigue caused by high false positive rates in AI alerts.
Recognize potential documentation inaccuracies from AI-generated text errors.
Address algorithmic biases that may perpetuate healthcare inequities if AI tools are used uncritically.
Patient & Prescribing Data
Patients with infectious diseases receiving AI-augmented clinical care
AI tools support antimicrobial prescribing by enforcing guidelines and providing decision support but require clinician oversight to avoid errors and biases.
Clinical Best Practices
Develop foundational AI literacy among ID clinicians to interpret, validate, and optimize AI tool use.
Set clear boundaries on automation to maintain human control over critical clinical decisions.
Promote interdisciplinary education and governance frameworks for responsible AI deployment in infectious disease practice.
Leverage AI to automate repetitive, searchable tasks to reduce clinician burnout and improve workflow efficiency.
Utilize AI-enabled hub-and-spoke and mobile-first models to expand access to ID expertise in underserved settings.
