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- Shandong Provincial Hospital, Jinan, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.