Abstract
AbstractThis 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
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