Predictive models and applicability of artificial intelligence-based approaches in drug allergy

Area of Science:

• Clinical immunology
• Medical informatics
• Artificial intelligence in medicine

Background:

• Drug allergy poses a significant public health burden and life threat.
• Diagnosis is challenging due to diverse clinical presentations, mechanisms, and test limitations.
• Current predictive models for drug allergy are limited.

Purpose of the Study:

• To review the utility of predictive models in drug allergy diagnosis.
• To explore the role of artificial intelligence (AI) in improving drug allergy management.
• To provide evidence for AI's potential in clinical settings.

Main Methods:

• Review of current literature on drug allergy diagnosis and predictive modeling.
• Analysis of emerging AI techniques, including machine learning and deep learning.
• Evaluation of AI's application in risk stratification and precision medicine.

Main Results:

• Artificial intelligence methods show potential for reliable drug allergy diagnosis and prediction.
• Machine learning, deep learning, and neural networks offer optimal models for clinical use.
• AI can enhance patient risk stratification and personalize treatment strategies.

Conclusions:

• Predictive models, particularly those leveraging AI, hold significant promise for drug allergy diagnosis.
• AI offers advanced tools to overcome current diagnostic challenges.
• The integration of AI can lead to improved patient classification and management.