Artificial Intelligence in Infectious Disease Clinical Practice: An Overview of Gaps, Opportunities, and Limitations

Area of Science:

• Clinical Medicine
• Infectious Diseases
• Artificial Intelligence

Background:

• Artificial intelligence (AI) integration is transforming clinical medicine.
• AI enhances diagnostic accuracy, therapeutic efficacy, and healthcare delivery.
• Infectious diseases present unique challenges for diagnosis and management.

Purpose of the Study:

• To review current applications, benefits, and limitations of AI in infectious diseases.
• To explore AI's role in diagnostics, clinical decision-making, and personalized medicine.
• To address AI's potential in disease surveillance, outbreak detection, and treatment optimization.

Main Methods:

• Literature review of AI applications in clinical and laboratory infectious diseases.
• Analysis of AI's impact on diagnostic speed and accuracy.
• Examination of AI's role in personalized treatment strategies and public health surveillance.

Main Results:

• AI demonstrates significant potential in rapid diagnosis, surveillance, and outbreak management.
• AI aids in optimizing treatment strategies for infectious diseases.
• Current AI use shows promise but faces limitations in data privacy, bias, and ethical considerations.

Conclusions:

• AI offers transformative potential for infectious disease diagnosis and management.
• Addressing data privacy, bias, and ethical concerns is crucial for effective AI deployment.
• Development of regulatory frameworks and inclusive databases is essential for equitable AI utilization.