Journal of Microbiology and Infection

AI and ML in Infectious Diseases

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Clinical microbiology is becoming more reliant on artificial intelligence and machine learning to enhance diagnosis, treatment, and the tracking of infectious diseases for epidemiological analysis, thereby providing significant improvements. Artificial intelligence and machine learning enhance the accuracy and speed of pathogen detection in diagnostic procedures. Examining extensive and intricate datasets, including genomic sequences and medical imaging, allows these technologies to rapidly pinpoint infectious agents, thereby leading to swift and precise diagnoses. Advanced AI models for analysing lung ultrasound videos have been created, leading to high levels of accuracy in diagnosing respiratory conditions.

Scientists use artificial intelligence to speed up the process of discovering new drugs in therapeutic development by predicting interactions between molecules of pathogens and potential antimicrobial medicines. Accelerating the identification of effective treatments helps address the urgent demand for new antibiotics, as antimicrobial resistance persists and continues to increase. Additionally, AI aids in predicting viral mutations, which in turn facilitates the creation of vaccines that remain effective against evolving strains. From an epidemiological viewpoint, AI enables real-time tracking and pandemic prediction by examining vast quantities of data from diverse sources. Machine learning algorithms have the capability to identify patterns and predict the spread of disease, enabling proactive public health measures to be put in place. Artificial intelligence methods have been utilised to forecast influenza trends and monitor the spread of illnesses like COVID-19, as a result enabling the allocation of resources and strategies for controlling outbreaks.

Clinical microbiology still encounters challenges in integrating artificial intelligence, despite advancements that have been made. Unlocking the full potential of artificial intelligence requires ensuring that data is accurate, using transparent algorithms, and conducting thorough real-world testing. Crucial to overcoming the challenges and ensuring the ethical use of artificial intelligence in disease management are interdisciplinary research and collaboration. AI and machine learning are transforming clinical microbiology, thereby increasing diagnostic accuracy, hastening therapeutic advancements, and improving epidemiological surveillance. Achieving innovative breakthroughs while adhering to strict ethical guidelines is crucial to fully unlocking the capabilities of these technologies in addressing infectious diseases.