An initiative to promote the integration of artificial intelligence in transforming the diagnosis and management of Wilson disease in the 21st century

Abstract

Wilson disease (WD) is a rare genetic disorder characterized by the excessive accumulation of copper in the body, leading to metabolic alterations and subsequent health complications due to mutations of the ATP7B gene. Despite advancements in medical science, diagnosing this condition remains challenging due to its variable presentation and overlap with other disorders. Traditional diagnostic methods are helpful in establishing WD, although in some cases, conflicting results may necessitate invasive procedures. The practicing physician may not always utilize the known diagnostic parameters or may not weigh their significance due to a lack of experience with the rare population of WD patients. In such cases, artificial intelligence (AI) can facilitate the diagnosis and management of WD, avoiding individual misinterpretation. By leveraging advanced algorithms and machine learning techniques, AI enhances diagnostic accuracy through predictive modeling and data analysis, while also facilitating personalized treatment plans tailored to individual patient profiles. Additionally, AI tools are reshaping disease monitoring through wearable technology and remote systems that provide real-time data integration with electronic health records. However, ethical considerations such as data privacy concerns and algorithmic bias must be addressed to ensure responsible implementation of AI in healthcare settings. The potential for broader applications across various genetic disorders further emphasizes the importance of continued research and interdisciplinary collaboration among tech developers, clinicians, and researchers. Ultimately, this paper highlights how AI can significantly improve patient outcomes and modulate copper metabolism in such a way that WD patients have a normal and worthwhile life expectancy.

Keywords

Artificial intelligence / Wilson disease / ATP7B / ATPase / copper / diagnostic trees / liver / brain

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