Toward Equitable Digital Mental Health: Integrating AI and Telepsychiatry in Global Practice
DOI:
https://doi.org/10.61978/digitus.v2i2.834Keywords:
Telepsychiatry, Artificial Intelligence, Mental Health Care, Digital Health Equity, Technology Adoption, Healthcare Innovation, Digital PsychiatryAbstract
In response to the growing mental health crisis and the expansion of digital healthcare, this narrative review explores the application of telepsychiatry and artificial intelligence (AI) in mental health services. The study aims to synthesize recent developments, challenges, and future directions in digital mental health innovation. A systematic literature search was conducted across PubMed, Scopus, and Web of Science databases, focusing on studies published between 2016 and 2023. Keywords such as "telepsychiatry," "mental health," "artificial intelligence," and "technology adoption" were used to identify relevant empirical and theoretical works. Inclusion criteria emphasized real-world applications and stakeholder perspectives. The results reveal substantial variability in the understanding and implementation of telepsychiatry across different regions and populations. Socioeconomic factors, digital literacy, and cultural perceptions significantly influence the acceptance and success of digital interventions. While AI-driven tools improve diagnostic efficiency and reduce treatment delays, systemic barriers such as regulatory limitations, institutional resistance, and data privacy concerns impede widespread adoption. Comparative analysis highlights a more favorable reception in high-income countries, though underserved populations in both developed and developing nations continue to face accessibility challenges. These findings underscore the urgent need for inclusive policies, capacity-building initiatives, and ethical AI governance frameworks. Addressing these factors can bridge existing gaps and ensure more equitable mental healthcare. The study concludes by emphasizing the importance of sustained interdisciplinary research to refine telepsychiatric models and promote socially responsible technology integration.
References
Adigwe, C., Mayeke, N., Olabanji, S., Okunleye, O., Joeaneke, P., & Olaniyi, O. (2024). The evolution of terrorism in the digital age... https://doi.org/10.9734/ajeba/2024/v24i31287 DOI: https://doi.org/10.9734/ajeba/2024/v24i31287
Adnan, M., Just, M., Baillie, L., & Kayacık, H. (2015). Investigating the work practices of network security professionals... https://doi.org/10.1108/ics-07-2014-0049 DOI: https://doi.org/10.1108/ICS-07-2014-0049
Akhriana, A. and Irmayana, A. (2019). Web app pendeteksi jenis serangan jaringan komputer... https://doi.org/10.33050/ccit.v12i1.604 DOI: https://doi.org/10.33050/ccit.v12i1.604
Al-Abassi, A., Karimipour, H., Dehghantanha, A., & Parizi, R. (2020). An ensemble deep learning-based cyber-attack detection... https://doi.org/10.1109/access.2020.2992249 DOI: https://doi.org/10.1109/ACCESS.2020.2992249
Alawi, M., Alsaqour, R., Abdalla, A., Abdelhaq, M., & Uddin, M. (2021). Multi-criteria prediction mechanism for vehicular wi-fi offloading... https://doi.org/10.32604/cmc.2021.018282 DOI: https://doi.org/10.32604/cmc.2021.018282
Alyas, T., Alissa, K., Alqahtani, M., Faiz, T., Alsaif, S., Tabassum, N., … & Naqvi, H. (2022). Multi-cloud integration security framework using honeypots... https://doi.org/10.1155/2022/2600712 DOI: https://doi.org/10.1155/2022/2600712
Asad, H. and Gashi, I. (2018). Diversity in open source intrusion detection systems... https://doi.org/10.1007/978-3-319-99130-6_18 DOI: https://doi.org/10.1007/978-3-319-99130-6_18
Balbin, D. and Lascano, E. (2023). Pandemic narratives of library and information centers in baguio-benguet... https://doi.org/10.1108/dlp-01-2023-0004 DOI: https://doi.org/10.1108/DLP-01-2023-0004
Castro-Toledo, F., Esteve, M., & Llinares, F. (2019). Fear of cybercrime... https://doi.org/10.31235/osf.io/kx26n DOI: https://doi.org/10.31235/osf.io/kx26n
Chandy, S., Rasekh, A., Barker, Z., & Shafiee, M. (2019). Cyberattack detection using deep generative models... https://doi.org/10.1061/(asce)wr.1943-5452.0001007 DOI: https://doi.org/10.1061/(ASCE)WR.1943-5452.0001007
Chen, B., Παππάς, Ν., Chen, Z., Yuan, D., & Zhang, J. (2019). Throughput and delay analysis... https://doi.org/10.1109/access.2019.2897017 DOI: https://doi.org/10.1109/ACCESS.2019.2897017
Avanzo, M., Stancanello, J., Pirrone, G., Drigo, A., & Retico, A. (2024). The evolution of artificial intelligence in medical imaging: from computer science to machine and deep learning. Cancers, 16(21), 3702. https://doi.org/10.3390/cancers16213702 DOI: https://doi.org/10.3390/cancers16213702
Bobkov, A., Cheng, F., Xu, J., Bobkova, T., Deng, F., He, J., … & Zheng, K. (2025). Telepsychiatry and artificial intelligence: a structured review of emerging approaches to accessible psychiatric care. Healthcare, 13(11), 1348. https://doi.org/10.3390/healthcare13111348 DOI: https://doi.org/10.3390/healthcare13111348
Endo, Y., Alaimo, L., Catalano, G., Chatzipanagiotou, O., & Pawlik, T. (2024). Application of artificial intelligence to hepatobiliary cancer clinical outcomes research. Artificial Intelligence Surgery, 4(2), 59-67. https://doi.org/10.20517/ais.2024.09 DOI: https://doi.org/10.20517/ais.2024.09
Laurent, G., Craynest, F., Thobois, M., & Hajjaji, N. (2023). Automatic classification of tumor response from radiology reports with rule-based natural language processing integrated into the clinical oncology workflow. JCO Clinical Cancer Informatics, (7). https://doi.org/10.1200/cci.22.00139 DOI: https://doi.org/10.1200/CCI.22.00139
Lin, H., Ni, L., Phuong, C., & Hong, J. (2024). Natural language processing for radiation oncology: personalizing treatment pathways. Pharmacogenomics and Personalized Medicine, 17, 65-76. https://doi.org/10.2147/pgpm.s396971 DOI: https://doi.org/10.2147/PGPM.S396971
Lončar-Turukalo, T., Zdravevski, E., Silva, J., Chouvarda, I., & Trajkovik, V. (2019). Literature on wearable technology for connected health: scoping review of research trends, advances, and barriers. Journal of Medical Internet Research, 21(9), e14017. https://doi.org/10.2196/14017 DOI: https://doi.org/10.2196/14017
Molenaar, A., Lukose, D., Brennan, L., Jenkins, E., & McCaffrey, T. (2024). Using natural language processing to explore social media opinions on food security: sentiment analysis and topic modeling study. Journal of Medical Internet Research, 26, e47826. https://doi.org/10.2196/47826 DOI: https://doi.org/10.2196/47826
Syriopoulou–Delli, C. (2025). Advances in autism spectrum disorder (asd) diagnostics: from theoretical frameworks to ai-driven innovations. Electronics, 14(5), 951. https://doi.org/10.3390/electronics14050951 DOI: https://doi.org/10.3390/electronics14050951
Yu, H., Deng, H., He, J., Keasling, J., & Luo, X. (2023). Unikp: a unified framework for the prediction of enzyme kinetic parameters. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-44113-1 DOI: https://doi.org/10.1038/s41467-023-44113-1
Thompson, M., Wei, Y., Calkin, D., O’Connor, C., Dunn, C., Anderson, N., … & Hogland, J. (2019). Risk management and analytics in wildfire response. Current Forestry Reports, 5(4), 226–239. https://doi.org/10.1007/s40725-019-00101-7 DOI: https://doi.org/10.1007/s40725-019-00101-7
Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. (2022). Comparison of Causal Forest and Regression-Based Approaches to Evaluate Treatment Effect Heterogeneity: An Application for Type 2 Diabetes Precision Medicine. https://doi.org/10.1101/2022.11.07.22282023 DOI: https://doi.org/10.1101/2022.11.07.22282023
Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests. Journal of the American Statistical Association, 113(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839 DOI: https://doi.org/10.1080/01621459.2017.1319839
Wang, L., & Michoel, T. (2017). Whole-Transcriptome Causal Network Inference With Genomic and Transcriptomic Data. https://doi.org/10.1101/213371 DOI: https://doi.org/10.1101/213371
Wang, Y., Goren, L., Zheng, D., & Zhang, H. (2021). Short Communication: Forward and Inverse Models Relating River Long Profile to Monotonic Step-Changes in Tectonic Rock Uplift Rate History: A Theoretical Perspective Under a Nonlinear Slope-Erosion Dependency. https://doi.org/10.5194/esurf-2021-101 DOI: https://doi.org/10.1002/essoar.10509168.1


