Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise

Area of Science:

• Medical Informatics
• Immunology
• Machine Learning

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly used in medicine.
• AI/ML shows promise for improving clinical practice and patient outcomes.
• Recent advancements focus on applying AI/ML to identify inborn errors of immunity (IEI).

Purpose of the Study:

• To provide a comprehensive review of AI/ML applications in the field of IEI.
• To cover the entire spectrum of AI/ML use, from data preprocessing to clinical implementation.
• To address challenges in using AI/ML for IEI diagnosis and management.

Main Methods:

• Review of existing literature on AI/ML in medicine and immunology.
• Analysis of AI/ML algorithms applied to electronic health record data for IEI identification.
• Examination of challenges in developing and implementing clinical decision support systems.

Main Results:

• AI/ML, combined with large datasets and advanced phenotyping, can enhance early IEI recognition.
• AI/ML models have the potential to improve the diagnosis and management of IEI.
• Implementation of AI/ML in clinical settings faces several challenges.

Conclusions:

• AI/ML holds significant potential to advance the early detection and equitable care of IEI.
• Further research and development are needed to overcome implementation challenges.
• AI/ML can refine the diagnosis and management of IEI, improving patient outcomes.