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Baiq Gita Haerunisa
Department of Medicine, Mataram University, Nusa Tenggara Barat, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Dentistry Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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The Role Of Artificial Intelligence As A Tool For Screening Depression And Anxiety Disorders Baiq Gita Haerunisa; Gede Tresna Ardiawan; Kirana Manggali Acintya Sasmoko; Lalu Hizrian Zikri Hamdi
International Journal of Health Engineering and Technology Vol. 5 No. 3 (2026): Vol. 5 No. 3 SEPTEMBER 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v5i3.1157

Abstract

Depression and anxiety disorders are common mental health disorders that contribute to the burden of disability. Early detection is still a challenge because screening through clinical interviews and questionnaires such as PHQ-9 and GAD-7 relies on subjective reports and requires professionals. The development of Artificial Intelligence (AI) offers opportunities to support more objective and efficient screening. To know the role of AI as a screening tool for depression and anxiety disorders and to examine its application, effectiveness, advantages, and limitations. This study used a literature review of the scientific literature on the use of AI in the detection and screening of depression and anxiety disorders. The literature was searched using keywords related to artificial intelligence, machine learning, deep learning, depression, anxiety, and screening, then analyzed descriptively based on data type, AI approach, screening capabilities, and its advantages and limitations. AI is able to recognize depression- and anxiety-related patterns through text, voice, video, wearables, and multimodal. The voice-based approach shows an AUC of around 0.92, wearables have a sensitivity of 0.89, a specificity of 0.93, and an AUC of 0.96, while multimodal shows a pooled AUC of around 0.95. AI has the potential to improve the objectivity and efficiency of screening, but it still faces limitations in data quality, privacy, external validation, and performance variations in different populations. AI has the potential to be a screening tool for depression and anxiety disorders, but it has not yet been able to replace clinical evaluation. Further validation and integration with health worker assessments are needed.
Co-Authors Gede Tresna Ardiawan Kirana Manggali Acintya Sasmoko Lalu Hizrian Zikri Hamdi