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Artificial intelligence in primary aldosteronism: current achievements and future challenges.

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Artificial intelligence (AI) enhances primary aldosteronism (PA) diagnosis and treatment. Machine learning improves screening efficiency and diagnostic accuracy, aiding personalized therapy and drug discovery, though clinical implementation faces challenges.

Keywords:
artificial intelligencediagnosismachine learningpredictive modelprimary aldosteronism

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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.