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Machine learning in infectious diseases: potential applications and limitations.

Ahmad Z Al Meslamani^1,2, Isidro Sobrino³, José de la Fuente^3,4

¹College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates.

Annals of Medicine

|June 10, 2024

Summary

Artificial Intelligence (AI) and Machine Learning (ML) offer powerful tools for tackling infectious diseases. These technologies aid in outbreak prediction, pathogen identification, and developing new treatments and vaccines.

Keywords:

Artificial intelligence Big Data infectious diseases machine learning vaccine

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Area of Science:

Infectious disease epidemiology
Computational biology
Bioinformatics

Background:

Infectious diseases pose a significant global threat to human and animal health.
Traditional methods struggle with the complexity and scale of modern infectious disease challenges.
Artificial Intelligence (AI) and Machine Learning (ML) present novel approaches for data analysis in this field.

Purpose of the Study:

To explore the applications and limitations of ML in managing infectious diseases.
To identify key challenges in areas like outbreak prediction, pathogen identification, drug discovery, and personalized medicine.
To propose solutions and highlight ML's role in identifying biomolecular targets for disease prevention and treatment.

Main Methods:

Review and commentary on the current state of AI and ML in infectious disease management.
Analysis of ML applications in outbreak prediction, pathogen identification, drug discovery, and personalized medicine.
Exploration of advanced techniques like Big Data analytics, catastrophic evolution event analysis, and vaccinomics.

Main Results:

ML demonstrates significant potential in analyzing diverse datasets for infectious disease control.
Identified challenges include data integration, model interpretability, and ethical considerations.
ML can accelerate the discovery of biomolecular targets and vaccine candidates.

Conclusions:

AI and ML are crucial for enhancing strategies in infectious disease management.
Future research should focus on overcoming current limitations to fully leverage ML's capabilities.
ML-driven insights are essential for proactive and effective infectious disease prevention and treatment.