Scope of artificial intelligence in the diagnosis and management of generalized anxiety disorder: A narrative review

Reeya Gamne; Sadhana Misar Wajpeyi; Shubham Bobade

doi:10.31893/multirev.2025280

Keywords:

artificial intelligence

generalized anxiety disorder

machine learning

mental health

natural language processing

Abstract

Generalized anxiety disorder (GAD) is a prevalent mental health condition characterized by excessive worry and anxiety, often impairing daily functioning and quality of life. The occurrence of Generalized Anxiety Disorder (GAD) is higher in females than in males. Traditional approaches to diagnosing and managing GAD rely heavily on subjective assessments, including self-report questionnaires and clinician evaluations, and resource-intensive therapeutic interventions such as cognitive-behavioral therapy (CBT) and medication. However, recent advancements in artificial intelligence (AI) have opened new and innovative avenues for enhancing mental healthcare delivery. AI-driven tools, including machine learning algorithms, natural language processing (NLP), and wearable sensors, offer promising solutions for the early and accurate diagnosis, personalized treatment, and remote monitoring of patients with GAD. AI technologies enable the analysis of speech, text, and physiological data to detect anxiety symptoms with remarkable precision, allowing for earlier interventions and more accurate clinical assessments. Furthermore, AI-powered chatbots, virtual reality therapies, and digital platforms provide scalable and cost-effective treatment options, improving accessibility to mental health services, especially in underserved or rural areas. Despite these benefits, the integration of AI in GAD care faces several challenges, including concerns about data privacy, algorithmic biases, and ethical considerations regarding the replacement of human therapists. This review explores the applications of AI in diagnosing and managing GAD, emphasizing its potential to transform mental health care practices. Key findings from the literature highlight AI’s ability to increase diagnostic accuracy, improve treatment efficiency, and increase patient engagement, whereas future research must address regulatory, implementation, and ethical barriers to fully realize its potential impact on GAD care.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Gamne, R., Wajpeyi, S. M., & Bobade, S. (2025). Scope of artificial intelligence in the diagnosis and management of generalized anxiety disorder: A narrative review. Multidisciplinary Reviews, 8(10), 2025280. https://doi.org/10.31893/multirev.2025280

[1] Abdulghafor, R., Abdelmohsen, A., Turaev, S., Ali, M. A., & Wani, S. (2022). An analysis of body language of patients using artificial intelligence. Healthcare, 10(12), 2504.

[2] Ahmadi, A., & RabieNezhad Ganji, N. (2023). AI-driven medical innovations: Transforming healthcare through data intelligence. International Journal of Bio Life Sciences (IJBLS), 2(2), 132–142.

[3] Bachina, L., & Kanagala, A. (2023). Health revolution: AI-powered patient engagement. International Journal of Chemical and Biochemical Sciences, 24(5), 2023.

[4] Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2016). Mental health smartphone apps: Review and evidence-based recommendations for future developments. JMIR Mental Health, 3(1), e4984.

[5] Bobade, S., Asutkar, S., Nagpure, D., & Kadav, A. (2025). A brief review of practical use of artificial intelligence in surgery in the current era. Multidisciplinary Reviews, 8(3), 2025085–2025085.

[6] Craske, M. G., & Tsao, J. C. (1999). Self-monitoring with panic and anxiety disorders. Psychological Assessment, 11(4), 466.

[7] Das, K. P., & Gavade, P. (2024). A review on the efficacy of artificial intelligence for managing anxiety disorders. Frontiers in Artificial Intelligence, 7, 1435895.

[8] De Choudhury, M., Pendse, S. R., & Kumar, N. (2023). Benefits and harms of large language models in digital mental health. arXiv preprint arXiv:2311.14693.

[9] DeMartini, J., Patel, G., & Fancher, T. L. (2019). Generalized anxiety disorder. Annals of Internal Medicine, 170(7), ITC49–ITC64.

[10] Duraivel, S. (2024). Enhancing virtual reality exposure therapy for social anxiety disorder using generative adversarial networks: A personalized and adaptive approach.

[11] Gamne, R., Misar, S., & Rai, M. (2024). Evaluation of comparative efficacy of Celastrus paniculatus (Jyotishmati) capsule versus sertraline capsule in the management of Chittodvega (generalized anxiety disorder): Protocol for a randomized controlled trial. F1000Research, 12, 1577.

[12] Gunter, R. W., & Whittal, M. L. (2010). Dissemination of cognitive-behavioral treatments for anxiety disorders: Overcoming barriers and improving patient access. Clinical Psychology Review, 30(2), 194–202.

[13] Maheu, M. M., Pulier, M. L., Wilhelm, F. H., McMenamin, J. P., & Brown-Connolly, N. E. (2004). The mental health professional and the new technologies: A handbook for practice today. Routledge.

[14] Malik, A. S., Acharya, S., & Humane, S. (2024). Exploring the impact of security technologies on mental health: A comprehensive review. Cureus, 16(2).

[15] Malik, P., Gang, U., Singh, T., Vyas, P., Singh, S., & Ramani, Y. (2023). Optimizing treatment planning and patient outcomes: The role of advanced analytics and personalized approaches in healthcare. International Journal of Modern Developments in Engineering and Science, 2(9), 20–24.

[16] Monti, J. M., & Monti, D. (2000). Sleep disturbance in generalized anxiety disorder and its treatment. Sleep Medicine Reviews, 4(3), 263–276.

[17] Nashwan, A. J., Gharib, S., Alhadidi, M., El-Ashry, A. M., Alamgir, A., Al-Hassan, M., ... & Abufarsakh, B. (2023). Harnessing artificial intelligence: Strategies for mental health nurses in optimizing psychiatric patient care. Issues in Mental Health Nursing, 44(10), 1020–1034.

[18] Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10).

[19] Nur, S. (2024). The role of digital health technologies and sensors in revolutionizing wearable health monitoring systems. International Journal of Innovative Research in Computer Science & Technology, 12(6), 69–80.

[20] Nwankwo, E. I., Emeihe, E. V., Ajegbile, M. D., Olaboye, J. A., & Maha, C. C. (2024). Integrating telemedicine and AI to improve healthcare access in rural settings. International Journal of Life Science Research Archive, 7(1), 59–77.

[21] Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with artificial intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health, 100099.

[22] Omarov, B., Zhumanov, Z., Gumar, A., & Kuntunova, L. (2023). Artificial intelligence enabled mobile chatbot psychologist using AIML and cognitive behavioral therapy. International Journal of Advanced Computer Science and Applications, 14(6).

[23] Pavlopoulos, A., Rachiotis, T., & Maglogiannis, I. (2024). An overview of tools and technologies for anxiety and depression management using AI. Applied Sciences, 14(19), 9068.

[24] Rook, L., Mazza, M. C., Lefter, I., & Brazier, F. (2022). Toward linguistic recognition of generalized anxiety disorder. Frontiers in Digital Health, 4, 779039.

[25] Shi, F., Zhou, F., Liu, H., Chen, L., & Ning, H. (2022). Survey and tutorial on hybrid human-artificial intelligence. Tsinghua Science and Technology, 28(3), 486–499.

[26] Tsvetanov, F. (2024). Integrating AI technologies into remote monitoring patient systems. Engineering Proceedings, 70(1), 54.

[27] van der Schyff, E. L., Ridout, B., Amon, K. L., Forsyth, R., & Campbell, A. J. (2023). Providing self-led mental health support through an artificial intelligence–powered chat bot (Leora) to meet the demand of mental health care. Journal of Medical Internet Research, 25, e46448.

[28] Velagaleti, S. B., Choukaier, D., Nuthakki, R., Lamba, V., Sharma, V., & Rahul, S. (2024). Empathetic algorithms: The role of AI in understanding and enhancing human emotional intelligence. Journal of Electrical Systems, 20(3s), 2051–2060.

[29] Zarate, D., Ball, M., Prokofieva, M., Kostakos, V., & Stavropoulos, V. (2023). Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Research, 330, 115579.

[30] Zhai, Y., Zhang, Y., Chu, Z., Geng, B., Almaawali, M., Fulmer, R., ... & Du, X. (2024). Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students. Journal of Counseling & Development.

Abstract

References

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