โก Quick Summary
This systematic review highlights the transformative role of artificial intelligence (AI) in the identification and management of inborn errors of immunity (IEI). AI methodologies have demonstrated high diagnostic accuracy and the potential to significantly reduce healthcare costs and diagnostic delays.
๐ Key Details
- ๐ Dataset: 23 retrospective/prospective studies and clinical trials
- ๐งฉ Focus: Application of AI in diagnosing and treating IEI
- โ๏ธ Technologies: AI tools integrating electronic health records (EHRs), clinical, immunological, and genetic data
- ๐ Performance: High diagnostic accuracy and improved detection of pathogenic mutations
๐ Key Takeaways
- ๐งฌ Inborn errors of immunity (IEI) are genetically driven disorders with diverse clinical manifestations.
- ๐ก AI tools enhance diagnostic accuracy and clinical decision-making for IEI.
- ๐ AI methodologies have shown improved detection of pathogenic mutations.
- ๐ Integration of data from EHRs accelerates the diagnostic process.
- ๐ฐ Cost-effectiveness of AI technologies can reduce healthcare expenses.
- โ ๏ธ Limitations include data bias and methodological inconsistencies among studies.
- ๐ Study conducted across pediatric and adult populations.
- ๐ PMID: 40943725.
๐ Background
Inborn errors of immunity (IEI) encompass a range of genetically driven disorders that compromise immune function. These disorders present with a wide variety of clinical symptoms, making early and accurate diagnosis a complex challenge. Traditional diagnostic methods often fall short due to their high costs and the need for functional validation, highlighting the necessity for innovative approaches in this field.
๐๏ธ Study
This systematic review analyzed data from four major databases, including PubMed and Scopus, to evaluate the application of AI techniques in the diagnosis and management of IEI. The review included 23 studies that explored the effectiveness of AI in both pediatric and adult populations, aiming to shed light on the current state and future potential of AI in this area.
๐ Results
The findings revealed that AI methodologies achieved high diagnostic accuracy and significantly improved the detection of pathogenic mutations. By effectively integrating and analyzing diverse data sources, AI tools not only accelerated the diagnostic process but also supported the development of personalized treatment strategies for patients with IEI.
๐ Impact and Implications
The implications of this study are profound. The integration of AI technologies in the early detection and management of IEI could lead to reduced diagnostic delays and lower healthcare costs. As AI continues to evolve, it holds the promise of transforming how we approach the diagnosis and treatment of complex immunological disorders, ultimately improving patient outcomes and quality of care.
๐ฎ Conclusion
This systematic review underscores the significant potential of AI in revolutionizing the identification and management of inborn errors of immunity. By enhancing diagnostic accuracy and facilitating personalized treatment, AI technologies are paving the way for a more efficient healthcare system. Continued research and development in this field are essential to address existing limitations and fully realize the benefits of AI in clinical practice.
๐ฌ Your comments
What are your thoughts on the role of AI in healthcare, particularly in the management of inborn errors of immunity? We invite you to share your insights and engage in a discussion! ๐ฌ Leave your comments below or connect with us on social media:
Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.
Abstract
Background: Inborn errors of immunity (IEI) are mainly genetically driven disorders that affect immune function and present with highly heterogeneous clinical manifestations, ranging from severe combined immunodeficiency (SCID) to adult-onset immune dysregulatory diseases. This clinical heterogeneity, coupled with limited awareness and the absence of a universal diagnostic test, makes early and accurate diagnosis challenging. Although genetic testing methods such as whole-exome and genome sequencing have improved detection, they are often expensive, complex, and require functional validation. Recently, artificial intelligence (AI) tools have emerged as promising for enhancing diagnostic accuracy and clinical decision-making for IEI. Methods: We conducted a systematic review of four major databases (PubMed, Scopus, Web of Science, and Embase) to identify peer-reviewed English-published studies focusing on the application of AI techniques in the diagnosis and treatment of IEI across pediatric and adult populations. Twenty-three retrospective/prospective studies and clinical trials were included. Results: AI methodologies demonstrated high diagnostic accuracy, improved detection of pathogenic mutations, and enhanced prediction of clinical outcomes. AI tools effectively integrated and analyzed electronic health records (EHRs), clinical, immunological, and genetic data, thereby accelerating the diagnostic process and supporting personalized treatment strategies. Conclusions: AI technologies show significant promise in the early detection and management of IEI by reducing diagnostic delays and healthcare costs. While offering substantial benefits, limitations such as data bias and methodological inconsistencies among studies must be addressed to ensure broader clinical applicability.
Author: [‘Taietti I’, ‘Votto M’, ‘Colaneri M’, ‘Passerini M’, ‘Leoni J’, ‘Marseglia GL’, ‘Licari A’, ‘Castagnoli R’]
Journal: J Clin Med
Citation: Taietti I, et al. Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review. Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review. 2025; 14:(unknown pages). doi: 10.3390/jcm14175958