Table 1.

Outlining some ways AI is used in the diagnosis of infectious diseases.

Application	Description	Reference
Detection of pathogens	AI algorithms can analyze biological samples, such as blood or tissue, to identify the presence of pathogens such as bacteria, viruses, or fungi	[39]
Predictive modeling	AI can use data from climate patterns, population movements, and historical infection rates to predict disease outbreaks. These predictive models assist healthcare providers in allocating resources and implementing preventive measures	[40]
Drug discovery	AI speeds up the drug discovery process by analyzing extensive datasets to identify potential drug candidates. It can predict the effectiveness and safety of compounds, thereby expediting the development of treatments for infectious diseases	[41, 42]
Diagnostic imaging analysis	AI algorithms are used to analyze medical images, including X-rays, CT scans, and MRIs. Their purpose is to detect signs of infectious diseases like pneumonia or tuberculosis. By doing so, these tools help radiologists with precise diagnosis and treatment planning	[43, 44]
Epidemiological surveillance	AI monitors online sources, social media, and news reports to track the real-time spread of infectious diseases. Through the analysis of big data, AI assists public health authorities in promptly responding to outbreaks and implementing precise interventions	[19, 45]
Antibiotic resistance prediction	AI algorithms analyze genetic data from pathogens to predict their resistance to antibiotics. This valuable information assists clinicians in choosing the most effective treatment for infectious diseases and plays a crucial role in the fight against antibiotic resistance	[46]
Remote monitoring	AI-powered wearable devices and mobile apps enable the remote monitoring of vital signs and symptoms related to infectious diseases. This allows patients to receive timely interventions, improves patient outcomes, and lowers healthcare costs	[47]