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Artificial intelligence (AI) shows promise for diagnosing rare diseases like inborn errors of immunity (IEI). However, challenges in data, implementation, and acceptance limit its widespread clinical use for IEI screening and care.

Keywords:
Artificial intelligenceclinical decision supportelectronic health recordsgenomicsimplementation scienceinborn errors of immunitymachine learningpredictive modelingprimary immunodeficiencyscreening algorithmsecondary immunodeficiency

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Area of Science:

  • Medical Informatics
  • Genetics and Genomics
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) offers potential for early diagnosis and screening, particularly for rare diseases like inborn errors of immunity (IEI).
  • The application of AI in healthcare, especially for rare conditions, is growing but faces significant hurdles.
  • Limited clinical utility and widespread implementation of AI for IEI diagnosis persist despite expanding research.

Purpose of the Study:

  • To comprehensively review current applications of AI in the diagnosis and care of IEI.
  • To identify and discuss the key challenges hindering the development and implementation of AI for IEI.
  • To provide an up-to-date overview of the field for researchers and clinicians.

Main Methods:

  • Literature review of AI applications in IEI diagnosis and care.
  • Analysis of challenges including dataset limitations, availability, harmonization, stakeholder acceptance, ethical/legal constraints, and technological barriers.
  • Synthesis of current research trends and future directions.

Main Results:

  • AI shows promise for screening and early diagnosis of rare diseases like IEI.
  • Significant challenges exist, including small dataset sizes, data availability and harmonization issues.
  • Barriers to real-world implementation include stakeholder acceptance, ethical/legal issues, and technological limitations.

Conclusions:

  • While AI research for IEI diagnosis is expanding, clinical utility and implementation remain limited.
  • Addressing data-related and implementation challenges is crucial for realizing AI's potential in IEI care.
  • Further research and collaboration are needed to overcome barriers and integrate AI effectively into IEI diagnosis and management.