Using artificial intelligence to improve body iron quantification: A scoping review
Abdulqadir J. Nashwan, Ibraheem M. Alkhawaldeh, Nour Shaheen, Ibrahem Albalkhi, Ibrahim Serag, Khalid Sarhan, Ahmad A. Abujaber, Alaa Abd‐Alrazaq, Mohamed A. Yassin
Abstract
This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.