Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects

Area of Science:

• Clinical Microbiology
• Bioinformatics
• Artificial Intelligence

Background:

• Conventional clinical microbiology methods are time-consuming and expensive.
• Artificial intelligence (AI) offers advanced data processing and analysis capabilities.
• Machine learning (ML) and deep learning (DL) algorithms are increasingly utilized in microbiology.

Purpose of the Study:

• To review recent AI applications in clinical microbiology.
• To educate clinical practitioners on ML algorithm implementation.
• To discuss challenges and future opportunities for AI in infectious disease epidemiology.

Main Methods:

• Utilizing diverse microbiological datasets including spectral analysis, microscopic images, and genomic/protein sequences.
• Training and evaluating various AI, ML, and DL algorithms.
• Integrating AI with quantitative structure-activity relationship (QSAR) models for antimicrobial discovery.

Main Results:

• AI algorithms minimize time, effort, and costs compared to conventional methods.
• AI has been crucial in vaccine development and discovering antiviral agents, including through drug repurposing.
• AI applications span spectral analysis, image analysis, and sequence data analysis.

Conclusions:

• AI significantly benefits clinical microbiology by enhancing efficiency and enabling novel discoveries.
• Challenges such as ethical considerations, data bias, and training errors need to be addressed for successful AI implementation.
• Future opportunities exist for AI in infectious disease epidemiology and antimicrobial resistance management.