Artificial intelligence in primary aldosteronism: current achievements and future challenges

Area of Science:

• Endocrinology
• Medical Informatics
• Artificial Intelligence

Background:

• Primary aldosteronism (PA) diagnosis and treatment are complex.
• Advances in artificial intelligence (AI) offer new tools for managing PA.
• Current diagnostic and therapeutic strategies for PA can be improved.

Purpose of the Study:

• To review the impact of AI on the diagnosis and treatment of PA.
• To highlight AI's role in screening, diagnosis, subtype classification, and treatment.
• To identify challenges and future directions for AI in PA management.

Main Methods:

• Review of recent literature on AI applications in PA.
• Analysis of machine learning models for PA screening and diagnosis.
• Evaluation of AI algorithms for PA subtype classification and treatment prediction.

Main Results:

• AI improves PA screening efficiency and diagnostic specificity.
• AI algorithms accurately classify PA subtypes using diverse data.
• Predictive models guide personalized PA therapy and aid drug discovery.
• Challenges include limited data, model interpretability, and real-world validation.

Conclusions:

• AI shows significant potential to revolutionize PA diagnosis and treatment.
• Further research requires larger datasets, interpretable models, and multicenter validation.
• Future efforts should focus on integrating AI into clinical pathways for PA management.