This comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential of medical intelligence [medical + artificial intelligence (AI)]. By addressing fundamental research questions, this study investigated the molecular and hormonal foundations underlying anxiety disorders, shedding light on the intricate interplay of genetic and hormonal factors contributing to the etiology and progression of anxiety. Furthermore, this review delves into the emerging implications of biomaterials, defibrillators, and state‐of‐the‐art devices for anxiety research, elucidating their potential roles in diagnosis, treatment, and patient management. A pivotal contribution of this review is the development and exploration of an AI‐driven model for real‐time cardiac signal analysis. This innovative approach offers a promising avenue for enhancing the precision and timeliness of anxiety diagnosis and monitoring. Leveraging machine learning and AI techniques enables the accurate classification of persons with anxiety based on real‐time cardiac data, thereby ushering in a new era of personalized and data‐driven mental health care. Identifying emerging themes and knowledge gaps lays the foundation for future research directions and offers a roadmap for scholars and practitioners to navigate this intricate field. In conclusion, this comprehensive review serves as a vital resource, consolidating diverse perspectives and fostering a deeper understanding of anxiety disorders from biological, engineering, and technological standpoints, ultimately contributing to advancing mental health research and clinical practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Classification

中文翻译：

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用于焦虑研究的医学智能：来自遗传学、激素、植入科学和智能设备的见解以及未来策略


这篇综合评论文章对焦虑研究进行了广泛的探索，探索了一个多方面的领域，融合了分子遗传学、激素影响、植入科学、再生工程和实时心脏信号分析等各个学科，同时利用了变革潜力医疗智能[医疗+人工智能(AI)]。通过解决基础研究问题，本研究调查了焦虑症的分子和激素基础，揭示了导致焦虑症病因和进展的遗传和激素因素之间错综复杂的相互作用。此外，这篇综述深入探讨了生物材料、除颤器和最先进的设备对焦虑研究的新影响，阐明了它们在诊断、治疗和患者管理中的潜在作用。本综述的一个关键贡献是开发和探索用于实时心脏信号分析的人工智能驱动模型。这种创新方法为提高焦虑诊断和监测的准确性和及时性提供了一条有前途的途径。利用机器学习和人工智能技术，可以根据实时心脏数据对焦虑症患者进行准确分类，从而开创个性化和数据驱动的心理保健新时代。确定新兴主题和知识差距为未来的研究方向奠定了基础，并为学者和从业者提供了探索这一复杂领域的路线图。 总之，这篇全面的综述是一个重要的资源，它整合了不同的观点，并从生物学、工程和技术的角度促进了对焦虑症的更深入的理解，最终有助于推进心理健康研究和临床实践。本文分类如下：应用领域 > 医疗保健应用领域 > 科技技术 > 分类