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REVIEW article

Front. Mol. Biosci.

Sec. Molecular Diagnostics and Therapeutics

Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1660588

This article is part of the Research TopicTransforming Chronic Disease Treatment with AI and Big DataView all 6 articles

Artificial Intelligence in Primary Aldosteronism: Current Achievements and Future Challenges

Provisionally accepted
Yisi  XuYisi XuBenjin  LiuBenjin LiuXuqi  HuangXuqi HuangXudong  GuoXudong GuoNing  SuoNing SuoShaobo  JiangShaobo JiangHanbo  WangHanbo Wang*
  • Shandong Provincial Hospital, Jinan, China

The final, formatted version of the article will be published soon.

Recent advances in artificial intelligence (AI) are reshaping the diagnostic and therapeutic of primary aldosteronism (PA). For screening, machine learning models integrate multidimensional data to improve the efficiency of PA detection, facilitating large-scale population screening. For diagnosis, AI-driven algorithms have further enhanced the specificity of PA identification. In subtype classification, AI algorithms achieve high predictive accuracy in differentiating PA subtypes through comprehensive analysis of clinical, imaging, and biochemical data, while simultaneously reducing reliance on invasive diagnostic procedures. Regarding treatment decision-making and outcome, predictive models guide personalized therapy by assessing treatment responses and surgical results. These models also contribute to discovering potential drugs by analyzing molecular targets computationally. Although scientists have achieved notable progress, there remain substantial challenges in clinical implementation, including limited sample size, insufficient model interpretability, and a lack of real-world validation. To translate technical advances into clinical practice, the field requires more reliable AI models with clear decision-making processes and rigorous multicenter validation studies. Future research should focus on clinical practice by developing integrated diagnostic-treatment pathways, while leveraging AI's strengths and overcoming its current limitations in generalizability and clinical acceptance.

Keywords: primary aldosteronism, artificial intelligence, machine learning, predictive model, diagnosis

Received: 06 Jul 2025; Accepted: 08 Aug 2025.

Copyright: © 2025 Xu, Liu, Huang, Guo, Suo, Jiang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hanbo Wang, Shandong Provincial Hospital, Jinan, China

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