Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care

References

• Abd-Alrazaq, A. A., Alajlani, M., Alalwan, A. A., Bewick, B. M., Gardner, P., & Househ, M. (2019). An overview of the features of chatbots in mental health: A scoping review. International Journal of Medical Informatics, 132, 103978. https://doi.org/10.1016/j.ijmedinf.2019.103978
   PubMed Web of Science ®Google Scholar
• Agyemang-Gyau, P. (2021). Artificial intelligence in healthcare and the implications for providers. On-Line Journal of Nursing Informatics, 25(2), 1–11. https://www.himss.org/resources/online-journal-nursing-informatics
   Google Scholar
• Ahmed, A., Agus, M., Alzubaidi, M., Aziz, S., Abd-Alrazaq, A., Giannicchi, A., & Househ, M. (2022). Overview of the role of big data in mental health: A scoping review. Computer Methods and Programs in Biomedicine Update, 2, 100076. https://doi.org/10.1016/j.cmpbup.2022.100076
   Google Scholar
• Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI (Vol. 60, p. 102387). Elsevier. https://doi.org/10.1016/j.ijinfomgt.2021.102387
   Google Scholar
• Al Jowf, G. I., Ahmed, Z. T., Reijnders, R. A., de Nijs, L., & Eijssen, L. M. (2023). To predict, prevent, and manage post-traumatic stress disorder (PTSD): A review of pathophysiology, treatment, and biomarkers. International Journal of Molecular Sciences, 24(6), 5238. https://doi.org/10.3390/ijms24065238
   PubMed Web of Science ®Google Scholar
• Alat, P., Das, S. S., Arora, A., & Jha, A. K. (2023). Mental health during COVID-19 lockdown in India: Role of psychological capital and internal locus of control. Current Psychology (New Brunswick, N.J.), 42(3), 1923–1935. https://doi.org/10.1007/s12144-021-01516-x
   PubMed Web of Science ®Google Scholar
• Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., Santamaría, J., Ouyang, C., Gupta, A., Gu, Y., & Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156–191. https://doi.org/10.1016/j.inffus.2023.03.008
   Web of Science ®Google Scholar
• Aleem, S., Huda, N. u., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022). Machine learning algorithms for depression: Diagnosis, insights, and research directions. Electronics, 11(7), 1111. https://doi.org/10.3390/electronics11071111
   Web of Science ®Google Scholar
• Alghatani, K., & Abdelmounaam, R. (2019, July). A cloud-based intelligent remote patient monitoring architecture. In Proceedings of the International Conference on Health Informatics & Medical Systems HIMS2019.
   Google Scholar
• Amanat, A., Rizwan, M., Javed, A. R., Abdelhaq, M., Alsaqour, R., Pandya, S., & Uddin, M. (2022). Deep learning for depression detection from textual data. Electronics, 11(5), 676. https://doi.org/10.3390/electronics11050676
   Web of Science ®Google Scholar
• American Nurses Association. (2001). Code of ethics for nurses with interpretive statements. Nursesbooks. org.
   Google Scholar
• Andersson, G. (2018). Internet interventions: Past, present and future. Internet Interventions, 12, 181–188. https://doi.org/10.1016/j.invent.2018.03.008
   PubMedGoogle Scholar
• Andrews, G., Cuijpers, P., Craske, M. G., McEvoy, P., & Titov, N. (2010). Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: A meta-analysis. PloS One, 5(10), e13196. https://doi.org/10.1371/journal.pone.0013196
   PubMed Web of Science ®Google Scholar
• Aurizki, G. E., & Wilson, I. (2022). Nurse‐led task‐shifting strategies to substitute for mental health specialists in primary care: A systematic review. International Journal of Nursing Practice, 28(5), e13046. https://doi.org/10.1111/ijn.13046
   PubMed Web of Science ®Google Scholar
• Babel, A., Taneja, R., Mondello Malvestiti, F., Monaco, A., & Donde, S. (2021). Artificial intelligence solutions to increase medication adherence in patients with non-communicable diseases. Frontiers in Digital Health, 3, 669869. https://doi.org/10.3389/fdgth.2021.669869
   PubMedGoogle Scholar
• Bajgain, B., Lorenzetti, D., Lee, J., & Sauro, K. (2023). Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: A scoping review protocol. BMJ Open, 13(2), e068373. https://doi.org/10.1136/bmjopen-2022-068373
   PubMed Web of Science ®Google Scholar
• Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
   PubMedGoogle Scholar
• Barrera, A., Gee, C., Wood, A., Gibson, O., Bayley, D., & Geddes, J. (2020). Introducing artificial intelligence in acute psychiatric inpatient care: Qualitative study of its use to conduct nursing observations. Evidence-Based Mental Health, 23(1), 34–38. https://doi.org/10.1136/ebmental-2019-300136
   PubMed Web of Science ®Google Scholar
• Bauman, S., & Rivers, I. (2023). Artificial intelligence, virtual reality, and online games. In S. Bauman & I. Rivers (Eds.), Mental health in the digital age (pp. 127–145). Springer International Publishing. https://doi.org/10.1007/978-3-031-32122-1_7
   Google Scholar
• Booth, R. G., Strudwick, G., McMurray, J., Chan, R., Cotton, K. D., & Cooke, S. (2021). The future of nursing informatics in a digitally-enabled world. In P. Hussey & M. A. Kennedy (Eds.), Introduction to nursing informatics (pp. 395–417). Springer Nature.
   Google Scholar
• Borah, B., Pathak, D., Sarmah, P., Som, B., & Nandi, S. (2019). Survey of textbased chatbot in perspective of recent technologies. In J. K. Mandal, S. Mukhopadhyay, P. Dutta, & K. Dasgupta (Eds.), Communications in computer and information science (pp. 84–96). Springer Nature. https://doi.org/10.1007/978-981-13-8581-0_7
   Google Scholar
• Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR Nursing, 4(1), e23933. https://doi.org/10.2196/23933
   PubMedGoogle Scholar
• Bzdok, D., & Yeo, B. T. T. (2017). Inference in the age of big data: Future perspectives on neuroscience. NeuroImage, 155, 549–564. https://doi.org/10.1016/j.neuroimage.2017.04.061
   PubMed Web of Science ®Google Scholar
• Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), 649–685. https://doi.org/10.1017/S1351324916000383
   Web of Science ®Google Scholar
• Chang, C. Y., Jen, H. J., & Su, W. S. (2022). Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management, 30(8), 3644–3653. https://doi.org/10.1111/jonm.13770
   PubMed Web of Science ®Google Scholar
• Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer, D., & Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 20(2), 154–170. https://doi.org/10.1002/wps.20882
   PubMed Web of Science ®Google Scholar
• Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet. Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-X
   PubMed Web of Science ®Google Scholar
• Chen, P. V., Helm, A., Caloudas, S. G., Ecker, A., Day, G., Hogan, J., & Lindsay, J. (2022). Evidence of phone vs video-conferencing for mental health treatments: A review of the literature. Current Psychiatry Reports, 24(10), 529–539. https://doi.org/10.1007/s11920-022-01359-8
   PubMed Web of Science ®Google Scholar
• Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S.-H. (2019). Review of machine learning algorithms for diagnosing mental illness. Psychiatry Investigation, 16(4), 262–269. https://doi.org/10.30773/pi.2018.12.21.2
   PubMed Web of Science ®Google Scholar
• Clancy, T. R. (2020). Artificial intelligence and nursing: The future is now. The Journal of Nursing Administration, 50(3), 125–127. https://doi.org/10.1097/NNA.0000000000000855
   PubMed Web of Science ®Google Scholar
• Cooper, A., & Ireland, D. (2018). Designing a chat-bot for non-verbal children on the autism spectrum. Studies in Health Technology and Informatics, 252, 63–68.
   PubMedGoogle Scholar
• Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
   PubMedGoogle Scholar
• Dawoodbhoy, F. M., Delaney, J., Cecula, P., Yu, J., Peacock, I., Tan, J., & Cox, B. (2021). AI in patient flow: Applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units. Heliyon, 7(5), e06993. https://doi.org/10.1016/j.heliyon.2021.e06993
   PubMed Web of Science ®Google Scholar
• de Filippis, R., Carbone, E. A., Gaetano, R., Bruni, A., Pugliese, V., Segura-Garcia, C., & De Fazio, P. (2019). Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: A systematic review. Neuropsychiatric Disease and Treatment, 15, 1605–1627. https://doi.org/10.2147/NDT.S202418
   PubMed Web of Science ®Google Scholar
• de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020). Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer’s disease: A systematic review. Journal of Alzheimer’s Disease: JAD, 78(4), 1547–1574. https://doi.org/10.3233/JAD-200888
   PubMed Web of Science ®Google Scholar
• Denecke, K., Abd-Alrazaq, A., & Househ, M. (2021). Artificial intelligence for Chatbots in mental health: Opportunities and challenges. In M. Househ, E. Borycki, & A. Kushniruk (Eds.), Multiple perspectives on artificial intelligence in healthcare: Opportunities and challenges (pp. 115–128). Springer Nature.
   Google Scholar
• Doraiswamy, P. M., Blease, C., & Bodner, K. (2020). Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artificial Intelligence in Medicine, 102, 101753. https://doi.org/10.1016/j.artmed.2019.101753
   PubMed Web of Science ®Google Scholar
• Ellahham, S., Ellahham, N., & Simsekler, M. C. E. (2020). Application of artificial intelligence in the health care safety context: Opportunities and challenges. American Journal of Medical Quality: The Official Journal of the American College of Medical Quality, 35(4), 341–348. https://doi.org/10.1177/1062860619878515
   PubMed Web of Science ®Google Scholar
• Elmasri, D., & Maeder, A. (2016, October 13–16). A conversational agent for an online mental health intervention. In Proceedings of the Brain Informatics and Health: International Conference, BIH 2016, Omaha, NE (pp. 243–251). Springer International Publishing. https://doi.org/10.1007/978-3-319-47103-7_24
   Google Scholar
• Erbe, D., Eichert, H. C., Riper, H., & Ebert, D. D. (2017). Blending face-to-face and internet-based interventions for the treatment of mental disorders in adults: Systematic review. Journal of Medical Internet Research, 19(9), e306. https://doi.org/10.2196/jmir.6588
   PubMed Web of Science ®Google Scholar
• Fakhoury, M. (2019). Frontiers in psychiatry: Artificial intelligence, precision medicine, and other paradigm shifts (pp. 119–125). Springer Nature.
   Google Scholar
• Farrell, A., George, N., Amado, S., & Wozniak, J. (2022). A systematic review of the literature on telepsychiatry for bipolar disorder. Brain and Behavior, 12(10), e2743. https://doi.org/10.1002/brb3.2743
   PubMed Web of Science ®Google Scholar
• Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., Koss, M. P., & Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245–258. https://doi.org/10.1016/s0749-3797(98)00017-8
   PubMed Web of Science ®Google Scholar
• Fenichel, M., Suler, J., Barak, A., Zelvin, E., Jones, G., Munro, K., Meunier, V., & Walker-Schmucker, W. (2002). Myths and realities of online clinical work. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 5(5), 481–497. https://doi.org/10.1089/109493102761022904
   PubMedGoogle Scholar
• Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216. https://doi.org/10.2196/13216
   PubMed Web of Science ®Google Scholar
• Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e7785. https://doi.org/10.2196/mental.7785
   Web of Science ®Google Scholar
• Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Musacchio, K. M., Jaroszewski, A. C., Chang, B. P., & Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187–232. https://doi.org/10.1037/bul0000084
   PubMed Web of Science ®Google Scholar
• Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018). Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: Randomized controlled trial. JMIR Mental Health, 5(4), e9782. https://doi.org/10.2196/mental.9782
   Web of Science ®Google Scholar
• Galatzer-Levy, I. R., Ma, S., Statnikov, A., Yehuda, R., & Shalev, A. Y. (2017). Utilization of machine learning for prediction of post-traumatic stress: A re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Translational Psychiatry, 7(3), e1070. https://doi.org/10.1038/tp.2017.38
   Web of Science ®Google Scholar
• Garcia-Vidal, C., Sanjuan, G., Puerta-Alcalde, P., Moreno-García, E., & Soriano, A. (2019). Artificial intelligence to support clinical decision-making processes. EBioMedicine, 46, 27–29. https://doi.org/10.1016/j.ebiom.2019.07.019
   PubMed Web of Science ®Google Scholar
• Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021). Accessing artificial intelligence for clinical decision-making. Frontiers in Digital Health, 3, 645232. https://doi.org/10.3389/fdgth.2021.645232
   PubMedGoogle Scholar
• Golden, G., Popescu, C., Israel, S., Perlman, K., Armstrong, C., Fratila, R., Tanguay-Sela, M., & Benrimoh, D. (2023). Applying artificial intelligence to clinical decision support in mental health: What have we learned? arXiv Preprint arXiv:2303.03511.
   Google Scholar
• Góngora Alonso, S., Marques, G., Agarwal, D., De la Torre Díez, I., & Franco-Martín, M. (2022). Comparison of machine learning algorithms in the prediction of hospitalized patients with schizophrenia. Sensors, 22(7), 2517. https://doi.org/10.3390/s22072517
   PubMed Web of Science ®Google Scholar
• Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21(11), 116. https://doi.org/10.1007/s11920-019-1094-0
   PubMed Web of Science ®Google Scholar
• Gratzer, D., & Khalid-Khan, F. (2016). Internet-delivered cognitive behavioural therapy in the treatment of psychiatric illness. CMAJ: Canadian Medical Association Journal = Journal de L'Association Medicale Canadienne, 188(4), 263–272. https://doi.org/10.1503/cmaj.150007
   PubMed Web of Science ®Google Scholar
• Grist, R., & Cavanagh, K. (2013). Computerised cognitive behavioural therapy for common mental health disorders, what works, for whom under what circumstances? A systematic review and meta-analysis. Journal of Contemporary Psychotherapy, 43(4), 243–251. https://doi.org/10.1007/s10879-013-9243-y
   Google Scholar
• Grove, C. (2021). Co-developing a mental health and wellbeing chatbot with and for young people. Frontiers in Psychiatry, 11, 1–12. https://doi.org/10.3389/fpsyt.2020.606041
   Web of Science ®Google Scholar
• Gual-Montolio, P., Jaén, I., Martínez-Borba, V., Castilla, D., & Suso-Ribera, C. (2022). Using artificial intelligence to enhance ongoing psychological interventions for emotional problems in real-or close to real-time: A systematic review. International Journal of Environmental Research and Public Health, 19(13), 7737. https://doi.org/10.3390/ijerph19137737
   PubMed Web of Science ®Google Scholar
• Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Behavioral Sciences, 18, 43–49. https://doi.org/10.1016/j.cobeha.2017.07.005
   Web of Science ®Google Scholar
• Halcomb, E. J., McInnes, S., Patterson, C., & Moxham, L. (2019). Nurse-delivered interventions for mental health in primary care: A systematic review of randomized controlled trials. Family Practice, 36(1), 64–71. https://doi.org/10.1093/fampra/cmy101
   PubMed Web of Science ®Google Scholar
• Hamdoun, S., Monteleone, R., Bookman, T., & Michael, K. (2023). AI-based and digital mental health apps: Balancing need and risk. IEEE Technology and Society Magazine, 42(1), 25–36. https://doi.org/10.1109/MTS.2023.3241309
   Web of Science ®Google Scholar
• Haque, M. D. R., & Rubya, S. (2023). An overview of chatbot-based mobile mental health apps: Insights from app description and user reviews. JMIR mHealth and uHealth, 11, e44838. https://doi.org/10.2196/44838
   PubMed Web of Science ®Google Scholar
• Haynos, A. F., Wang, S. B., Lipson, S., Peterson, C. B., Mitchell, J. E., Halmi, K. A., Agras, W. S., & Crow, S. J. (2021). Machine learning enhances prediction of illness course: A longitudinal study in eating disorders. Psychological Medicine, 51(8), 1392–1402. https://doi.org/10.1017/S0033291720000227
   PubMed Web of Science ®Google Scholar
• Hedman, E., Ljótsson, B., & Lindefors, N. (2012). Cognitive behavior therapy via the Internet: A systematic review of applications, clinical efficacy and cost–effectiveness. Expert Review of Pharmacoeconomics & Outcomes Research, 12(6), 745–764. https://doi.org/10.1586/erp.12.67
   PubMed Web of Science ®Google Scholar
• Hofmann, S. G., & Smits, J. A. J. (2008). Cognitive-behavioral therapy for adult anxiety disorders. The Journal of Clinical Psychiatry, 69(4), 621–632. https://doi.org/10.4088/jcp.v69n0415
   PubMed Web of Science ®Google Scholar
• Hollinghurst, S., Peters, T. J., Kaur, S., Wiles, N., Lewis, G., & Kessler, D. (2010). Cost-effectiveness of therapist-delivered online cognitive–behavioural therapy for depression: Randomised controlled trial. The British Journal of Psychiatry: The Journal of Mental Science, 197(4), 297–304. https://doi.org/10.1192/bjp.bp.109.073080
   PubMed Web of Science ®Google Scholar
• Humphrey, B. A. (2021). Data privacy vs. innovation: A quantitative analysis of artificial intelligence in healthcare and its impact on HIPAA regarding the privacy and security of protected health information. Robert Morris University.
   Google Scholar
• Hungerbuehler, I., Daley, K., Cavanagh, K., Garcia Claro, H., & Kapps, M. (2021). Chatbot-based assessment of employees’ mental health: Design process and pilot implementation. JMIR Formative Research, 5(4), e21678. https://doi.org/10.2196/21678
   PubMedGoogle Scholar
• Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 6(11), e12106. https://doi.org/10.2196/12106
   PubMed Web of Science ®Google Scholar
• Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2021). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1), 214–226. https://doi.org/10.1109/TCSS.2020.3021467
   Web of Science ®Google Scholar
• Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), e000101. https://doi.org/10.1136/svn-2017-000101
   Google Scholar
• Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
   Google Scholar
• Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
   PubMed Web of Science ®Google Scholar
• Kamran Ul Haq, A., Khattak, A., Jamil, N., Naeem, M. A., & Mirza, F. (2020). Data analytics in mental healthcare. Scientific Programming, 2020, 1–9. https://doi.org/10.1155/2020/2024160
   Web of Science ®Google Scholar
• Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, 106848. https://doi.org/10.1016/j.compbiomed.2023.106848
   PubMed Web of Science ®Google Scholar
• Krech, S. (2019). Medical big data analytics and smart internet of things-enabled mobile-based health monitoring systems. American Journal of Medical Research, 6(2), 31–36.
   Google Scholar
• Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ‐9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
   PubMed Web of Science ®Google Scholar
• Kulasinghe, S. A. S. A., Jayasinghe, A., Rathnayaka, R. M. A., Karunarathne, P. B. M. M. D., Suranjini Silva, P. D., & Anuradha Jayakodi, J. A. D. C. (2019). AI based depression and suicide prevention system [Paper presentation]. 2019 International Conference on Advancements in Computing (ICAC). https://doi.org/10.1109/ICAC49085.2019.9103411
   Google Scholar
• Kumar, A., Kearney, A., Hoskins, K., & Iyengar, A. (2020). The role of psychiatric mental health nurse practitioners in improving mental and behavioral health care delivery for children and adolescents in multiple settings. Archives of Psychiatric Nursing, 34(5), 275–280. https://doi.org/10.1016/j.apnu.2020.07.022
   PubMed Web of Science ®Google Scholar
• Lancet, T. (2022). Mental health after China’s prolonged lockdowns. Lancet (London, England), 399(10342), 2167. https://doi.org/10.1016/s0140-6736(22)01051-0
   PubMed Web of Science ®Google Scholar
• Lazem, S., Giglitto, D., Nkwo, M. S., Mthoko, H., Upani, J., & Peters, A. (2022). Challenges and paradoxes in decolonising HCI: A critical discussion. Computer Supported Cooperative Work (CSCW), 31(2), 159–196. https://doi.org/10.1007/s10606-021-09398-0
   Web of Science ®Google Scholar
• Lee, E. E., Torous, J., De Choudhury, M., Depp, C. A., Graham, S. A., Kim, H. C., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021). Artificial intelligence for mental health care: Clinical applications, barriers, facilitators, and artificial wisdom. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 6(9), 856–864. https://doi.org/10.1016/j.bpsc.2021.02.001
   PubMedGoogle Scholar
• Lee, Y., Cui, Y., Jamieson, J., Fu, W., & Yamashita, N. (2023). Exploring effects of chatbot-based social contact on reducing mental illness stigma [Paper presentation]. Conference on Human Factors in Computing Systems, Germany.
   Google Scholar
• Lehavot, K., Katon, J. G., Chen, J. A., Fortney, J. C., & Simpson, T. L. (2018). Post-traumatic stress disorder by gender and veteran status. American Journal of Preventive Medicine, 54(1), e1–e9. https://doi.org/10.1016/j.amepre.2017.09.008
   PubMed Web of Science ®Google Scholar
• Leichsenring, F., & Abbass, A. (2013). Personality-guided therapy for depression: A randomized controlled trial. Journal of the American Medical Association, 310(20), 2157–2158. https://doi.org/10.1001/jama.2013.281053
   Web of Science ®Google Scholar
• Lekadir, K., Quaglio, G., Garmendia, A. T., & Gallin, C. (2022). Artificial intelligence in healthcare applications, risks, and ethical and societal impacts. European Parliamentary Research Service (EPRS).
   Google Scholar
• Lovejoy, C. A., Buch, V., & Maruthappu, M. (2019). Technology and mental health: The role of artificial intelligence. European Psychiatry: The Journal of the Association of European Psychiatrists, 55, 1–3. https://doi.org/10.1016/j.eurpsy.2018.08.004
   PubMed Web of Science ®Google Scholar
• Lucas, G., Rizzo, A., Gratch, J., & Morency, L. (2017). Reporting mental health symptoms: Breaking down barriers to care with virtual human interviewers. Frontier in Robotics and AI, 4(51), 1–9.
   Google Scholar
• Mano, L. Y., Mazzo, A., Neto, J. R. T., Meska, M. H. G., Giancristofaro, G. T., Ueyama, J., & Júnior, G. A. P. (2019). Using emotion recognition to assess simulation-based learning. Nurse Education in Practice, 36, 13–19. https://doi.org/10.1016/j.nepr.2019.02.017
   PubMed Web of Science ®Google Scholar
• McCaughan, E., Parahoo, K., Hueter, I., Northouse, L., & Bradbury, I. (2017). Online support groups for women with breast cancer. The Cochrane Database of Systematic Reviews, 3(3), CD011652. https://doi.org/10.1002/14651858.CD011652.pub2
   PubMed Web of Science ®Google Scholar
• Medina, R., Bouhaben, J., de Ramón, I., Cuesta, P., Antón-Toro, L., Pacios, J., Quintero, J., Ramos-Quiroga, J. A., & Maestú, F. (2021). Electrophysiological brain changes associated with cognitive improvement in a pediatric attention deficit hyperactivity disorder digital artificial intelligence-driven intervention: Randomized controlled trial. Journal of Medical Internet Research, 23(11), e25466. https://doi.org/10.2196/25466
   PubMed Web of Science ®Google Scholar
• Minerva, F., & Giubilini, A. (2023). Is AI the future of mental healthcare? Topoi: An International Review of Philosophy, 42(3), 1–9. https://doi.org/10.1007/s11245-023-09932-3
   PubMed Web of Science ®Google Scholar
• Moukaddam, N., Sano, A., Salas, R., Hammal, Z., & Sabharwal, A. (2022). Turning data into better mental health: Past, present, and future. Frontiers in Digital Health, 4, 916810. https://doi.org/10.3389/fdgth.2022.916810
   PubMedGoogle Scholar
• Mureşan, V., Montgomery, G. H., & David, D. (2012). Emotional outcomes and mechanisms of change in online cognitive-behavioral interventions: A quantitative meta-analysis of clinical controlled studies. Journal of Technology in Human Services, 30(1), 1–13. https://doi.org/10.1080/15228835.2011.653290
   Google Scholar
• Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Frontiers in Surgery, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322
   PubMed Web of Science ®Google Scholar
• Nashwan, A. J. (2023). A new era in cardiometabolic management: Unlocking the potential of artificial intelligence for improved patient outcomes. Endocrine Practice: Official Journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 29(9), 743–745. https://doi.org/10.1016/j.eprac.2023.06.003
   PubMed Web of Science ®Google Scholar
• Nashwan, A. J., Abujaber, A. A., & AbuJaber, A. (2023). Harnessing large language models in nursing care planning: Opportunities, challenges, and ethical considerations. Cureus, 15(6), e40542. https://doi.org/10.7759/cureus.40542
   PubMedGoogle Scholar
• National Institute of Mental Health (NIMH). (2023). Strategic Plan 2020-2023. NIMH.
   Google Scholar
• Nguyen, T., O’Dea, B., Larsen, M., Phung, D., Venkatesh, S., & Christensen, H. (2017). Using linguistic and topic analysis to classify sub-groups of online depression communities. Multimedia Tools and Applications, 76(8), 10653–10676. https://doi.org/10.1007/s11042-015-3128-x
   Web of Science ®Google Scholar
• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.), 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
   PubMed Web of Science ®Google Scholar
• Oladeji, B. D., & Gureje, O. (2016). Brain drain: A challenge to global mental health. BJPsych International, 13(3), 61–63. https://doi.org/10.1192/s2056474000001240
   PubMedGoogle Scholar
• Panchal, N., Heather, S., Rudowitz, R., & Cox, C. (2023). The implications of COVID-19 for mental health and substance use. Kaiser Family Foundation.
   Google Scholar
• Papachristou, N., Kotronoulas, G., Dikaios, N., Allison, S. J., Eleftherochorinou, H., Rai, T., Kunz, H., Barnaghi, P., Miaskowski, C., & Bamidis, P. D. (2023). Digital transformation of cancer care in the era of big data, artificial intelligence and data-driven interventions: Navigating the field. Seminars in Oncology Nursing, 39(3), 151433. https://doi.org/10.1016/j.soncn.2023.151433
   PubMed Web of Science ®Google Scholar
• Parker, W., Jaremko, J. L., Cicero, M., Azar, M., El-Emam, K., Gray, B. G., Hurrell, C., Lavoie-Cardinal, F., Desjardins, B., Lum, A., Sheremeta, L., Lee, E., Reinhold, C., Tang, A., & Bromwich, R. (2021). Canadian Association of Radiologists White Paper on De-identification of medical imaging: Part 2, practical considerations. Canadian Association of Radiologists Journal = Journal L'Association Canadienne Des Radiologistes, 72(1), 25–34. https://doi.org/10.1177/0846537120967345
   PubMed Web of Science ®Google Scholar
• Perez-Pozuelo, I., Spathis, D., Clifton, E. A., & Mascolo, C. (2021). Wearables, smartphones, and artificial intelligence for digital phenotyping and health. In S. Syed-Abdul, X. Zhu, & L. Fernandez-Luque (Eds.), Digital health (pp. 33–54). Elsevier.
   Google Scholar
• Pham, K. T., Nabizadeh, A., & Selek, S. (2022). Artificial intelligence and chatbots in psychiatry. The Psychiatric Quarterly, 93(1), 249–253. https://doi.org/10.1007/s11126-022-09973-8
   PubMed Web of Science ®Google Scholar
• Phan, P., Mitragotri, S., & Zhao, Z. (2023). Digital therapeutics in the clinic. Bioengineering & Translational Medicine, 8(4), e10536. https://doi.org/10.1002/btm2.10536
   PubMed Web of Science ®Google Scholar
• Philip, P., Micoulaud-Franchi, J.-A., Sagaspe, P., Sevin, E. D., Olive, J., Bioulac, S., & Sauteraud, A. (2017). Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Scientific Reports, 7(1), 42656. https://doi.org/10.1038/srep42656
   PubMed Web of Science ®Google Scholar
• Pinto, M. D., Hickman, R. L., Clochesy, J., & Buchner, M. (2013). Avatar-based depression self-management technology: Promising approach to improve depressive symptoms among young adults. Applied Nursing Research: ANR, 26(1), 45–48. https://doi.org/10.1016/j.apnr.2012.08.003
   PubMed Web of Science ®Google Scholar
• Prochaska, J. J., Vogel, E. A., Chieng, A., Kendra, M., Baiocchi, M., Pajarito, S., & Robinson, A. (2021). A therapeutic relational agent for reducing problematic substance use (Woebot): Development and usability study. Journal of Medical Internet Research, 23(3), e24850. https://doi.org/10.2196/24850
   PubMed Web of Science ®Google Scholar
• Reger, M. A., & Gahm, G. A. (2009). A meta‐analysis of the effects of internet‐and computer‐based cognitive‐behavioral treatments for anxiety. Journal of Clinical Psychology, 65(1), 53–75. https://doi.org/10.1002/jclp.20536
   PubMed Web of Science ®Google Scholar
• Richards, P., & Simpson, S. (2015). Beyond the therapeutic hour: An exploratory pilot study of using technology to enhance alliance and engagement within face-to-face psychotherapy. British Journal of Guidance & Counselling, 43(1), 57–93. https://doi.org/10.1080/03069885.2014.936824
   Web of Science ®Google Scholar
• Roa, T. M., Biller-Andorno, N., & Trachsel, M. (2021). The ethics of artificial intelligence in psychotherapy. In M. Trachsel, J.s Gaab, N. Biller-Andorno, Ş. Tekin, & J. Z. Sadler (Eds.), The Oxford handbook of psychotherapy ethics (pp. 1–19). Oxford University Press.
   Google Scholar
• Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30–39. https://doi.org/10.1097/01.NUMA.0000578988.56622.21
   PubMedGoogle Scholar
• Rosenfeld, A., Benrimoh, D., Armstrong, C., Mirchi, N., Langlois-Therrien, T., Rollins, C., Tanguay-Sela, M., Mehltretter, J., Fratila, R., Israel, S., Snook, E., Perlman, K., Kleinerman, A., Saab, B., Thoburn, M., Gabbay, C., & Yaniv-Rosenfeld, A. (2021). 6 - Big Data analytics and artificial intelligence in mental healthcare. In A. Khanna, D. Gupta, & N. Dey (Eds.), Applications of big data in healthcare (pp. 137–171). Academic Press. https://doi.org/10.1016/B978-0-12-820203-6.00001-1
   Google Scholar
• Rubeis, G. (2022). iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interventions, 28, 100518. https://doi.org/10.1016/j.invent.2022.100518
   PubMedGoogle Scholar
• Samy Helmy, B., & Samy Helmy, A. (2022). Role of artificial intelligence in mental wellbeing: Opportunities and challenges. Journal of Artificial Intelligence, 15(1), 1–8. https://doi.org/10.3923/jai.2022.1.8
   Google Scholar
• Schwartz, R. C., & Blankenship, D. M. (2014). Racial disparities in psychotic disorder diagnosis: A review of empirical literature. World Journal of Psychiatry, 4(4), 133–140. https://doi.org/10.5498/wjp.v4.i4.133
   PubMedGoogle Scholar
• Seibert, K., Domhoff, D., Fürstenau, D., Biessmann, F., Schulte-Althoff, M., & Wolf-Ostermann, K. (2023). Exploring needs and challenges for AI in nursing care–results of an explorative sequential mixed methods study. BMC Digital Health, 1(1), 1–17. https://doi.org/10.1186/s44247-023-00015-2
   Google Scholar
• Sheikh, M., Qassem, M., & Kyriacou, P. A. (2021). Wearable, environmental, and smartphone-based passive sensing for mental health monitoring. Frontiers in Digital Health, 3, 662811. https://doi.org/10.3389/fdgth.2021.662811
   PubMedGoogle Scholar
• Sickel, A. E., Seacat, J. D., & Nabors, N. A. (2014). Mental health stigma update: A review of consequences. Advances in Mental Health, 12(3), 202–215. https://doi.org/10.1080/18374905.2014.11081898
   Google Scholar
• Siegel, A., Zuo, Y., Moghaddamcharkari, N., McIntyre, R. S., & Rosenblat, J. D. (2021). Barriers, benefits and interventions for improving the delivery of telemental health services during the coronavirus disease 2019 pandemic: A systematic review. Current Opinion in Psychiatry, 34(4), 434–443. https://doi.org/10.1097/YCO.0000000000000714
   PubMed Web of Science ®Google Scholar
• Simhadri Apparao Polireddi, N., & Kavitha, J. (2023). Effectiveness of automated chatbots for operational support and self-service based on fuzzy classifier for ASD. Soft Computing, 1–8. https://doi.org/10.1007/s00500-023-08541-x
   Web of Science ®Google Scholar
• Simon, G. E., Johnson, E., Lawrence, J. M., Rossom, R. C., Ahmedani, B., Lynch, F. L., Beck, A., Waitzfelder, B., Ziebell, R., Penfold, R. B., & Shortreed, S. M. (2018). Predicting suicide attempts and suicide deaths following outpatient visits using electronic health records. The American Journal of Psychiatry, 175(10), 951–960. https://doi.org/10.1176/appi.ajp.2018.17101167
   PubMed Web of Science ®Google Scholar
• Smith, H. (2021). Clinical AI: Opacity, accountability, responsibility and liability. AI & Society, 36(2), 535–545. https://doi.org/10.1007/s00146-020-01019-6
   Web of Science ®Google Scholar
• Spek, V., Cuijpers, P. I. M., Nyklíček, I., Riper, H., Keyzer, J., & Pop, V. (2007). Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: A meta-analysis. Psychological Medicine, 37(3), 319–328. https://doi.org/10.1017/S0033291706008944
   PubMed Web of Science ®Google Scholar
• Spitzer, R. L., Williams, J., & Kroenke, K. (2015). GAD-7.
   Google Scholar
• Steinkamp, J. M., Goldblatt, N., Borodovsky, J. T., LaVertu, A., Kronish, I. M., Marsch, L. A., & Schuman-Olivier, Z. (2019). Technological interventions for medication adherence in adult mental health and substance use disorders: A systematic review. JMIR Mental Health, 6(3), e12493. https://doi.org/10.2196/12493
   PubMed Web of Science ®Google Scholar
• Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021). Blockchain and artificial intelligence technology in e-Health. Environmental Science and Pollution Research International, 28(38), 52810–52831. https://doi.org/10.1007/s11356-021-16223-0
   PubMed Web of Science ®Google Scholar
• Tanioka, T., Yasuhara, Y., Dino, M. J. S., Kai, Y., Locsin, R. C., & Schoenhofer, S. O. (2019). Disruptive engagements with technologies, robotics, and caring: Advancing the transactive relationship theory of nursing. Nursing Administration Quarterly, 43(4), 313–321. https://doi.org/10.1097/NAQ.0000000000000365
   PubMedGoogle Scholar
• Tao, X., Zhu, W., Chu, M., & Zhang, Y. (2023). Nurse-led virtual interventions in managing chronic diseases: A protocol for a systematic review of randomised controlled trials. BMJ Open, 13(5), e070583. https://doi.org/10.1136/bmjopen-2022-070583
   PubMed Web of Science ®Google Scholar
• Thieme, A., Belgrave, D., & Doherty, G. (2020). Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction, 27(5), 1–53. https://doi.org/10.1145/3398069
   Web of Science ®Google Scholar
• Thieme, A., Hanratty, M., Lyons, M., Palacios, J., Marques, R. F., Morrison, C., & Doherty, G. (2023). Designing human-centered AI for mental health: Developing clinically relevant applications for online CBT treatment. ACM Transactions on Computer-Human Interaction, 30(2), 1–50. https://doi.org/10.1145/3564752
   Web of Science ®Google Scholar
• Timan, T., & Mann, Z. (2021). Data protection in the era of artificial intelligence: Trends, existing solutions and recommendations for privacy-preserving technologies. In E. Curry, A. Metzger, S. Zillner, J.-C. Pazzaglia, & A. G. Robles (Eds.), The elements of big data value: Foundations of the research and innovation ecosystem (pp. 153–175). Springer International Publishing.
   Google Scholar
• Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023). A call to action on assessing and mitigating bias in artificial intelligence applications for mental health. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 18(5), 1062–1096. https://doi.org/10.1177/17456916221134490
   PubMedGoogle Scholar
• Torous, J., Keshavan, M., & Gutheil, T. (2014). Promise and perils of digital psychiatry. Asian Journal of Psychiatry, 10, 120–122. https://doi.org/10.1016/j.ajp.2014.06.006
   PubMedGoogle Scholar
• Treanor, C. J., Kouvonen, A., Lallukka, T., & Donnelly, M. (2021). Acceptability of computerized cognitive behavioral therapy for adults: Umbrella review. JMIR Mental Health, 8(7), e23091. https://doi.org/10.2196/23091
   PubMed Web of Science ®Google Scholar
• Tremain, H., McEnery, C., Fletcher, K., & Murray, G. (2020). The therapeutic alliance in digital mental health interventions for serious mental illnesses: Narrative review. JMIR Mental Health, 7(8), e17204. https://doi.org/10.2196/17204
   PubMed Web of Science ®Google Scholar
• Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2021). The ethics of algorithms: Key problems and solutions. In K. S. Gill (Ed.), Ethics, governance, and policies in artificial intelligence (pp. 97–123). Springer Nature.
   Google Scholar
• Tutun, S., Johnson, M. E., Ahmed, A., Albizri, A., Irgil, S., Yesilkaya, I., Ucar, E. N., Sengun, T., & Harfouche, A. (2023). An AI-based decision support system for predicting mental health disorders. Information Systems Frontiers: A Journal of Research and Innovation, 25(3), 1261–1276. https://doi.org/10.1007/s10796-022-10282-5
   PubMed Web of Science ®Google Scholar
• Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: A review of the psychiatric landscape. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 64(7), 456–464. https://doi.org/10.1177/0706743719828977
   PubMed Web of Science ®Google Scholar
• Vandemeulebroucke, T., de Casterlé, B. D., & Gastmans, C. (2018). The use of care robots in aged care: A systematic review of argument-based ethics literature. Archives of Gerontology and Geriatrics, 74, 15–25. https://doi.org/10.1016/j.archger.2017.08.014
   PubMed Web of Science ®Google Scholar
• Wang, K. Z., Bani-Fatemi, A., Adanty, C., Harripaul, R., Griffiths, J., Kolla, N., Gerretsen, P., Graff, A., & De Luca, V. (2020). Prediction of physical violence in schizophrenia with machine learning algorithms. Psychiatry Research, 289, 112960. https://doi.org/10.1016/j.psychres.2020.112960
   PubMed Web of Science ®Google Scholar
• Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
   Web of Science ®Google Scholar
• Whiteford, H. A., Ferrari, A. J., Degenhardt, L., Feigin, V., & Vos, T. (2015). The global burden of mental, neurological and substance use disorders: An analysis from the global burden of disease study 2010. PLoS One, 10(2), e0116820. https://doi.org/10.1371/journal.pone.0116820
   PubMed Web of Science ®Google Scholar
• Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. https://doi.org/10.1038/s41591-019-0548-6
   PubMed Web of Science ®Google Scholar
• Wilson, R. L., Higgins, O., Atem, J., Donaldson, A. E., Gildberg, F. A., Hooper, M., Hopwood, M., Rosado, S., Solomon, B., Ward, K., & Welsh, B. (2023). Artificial intelligence: An eye cast towards the mental health nursing horizon. International Journal of Mental Health Nursing, 32(3), 938–944. https://doi.org/10.1111/inm.13121
   PubMed Web of Science ®Google Scholar
• Woebot Health. (2023). Wysa.
   Google Scholar
• World Health Organization. (2021). Mental Health ATLAS 2020. World Health Organization.
   Google Scholar
• World Health Organization. (2022). World mental health report: Transforming mental health for all. World Health Organization.
   Google Scholar
• Yan, W. J., Ruan, Q. N., & Jiang, K. (2022). Challenges for artificial intelligence in recognizing mental disorders. Diagnostics (Basel, Switzerland), 13(1), 1–10. https://doi.org/10.3390/diagnostics13010002
   PubMedGoogle Scholar
• Yasavur, U., Lisetti, C., & Rishe, N. (2014). Let’s talk! Speaking virtual counselor offers you a brief intervention. Journal on Multimodal User Interfaces, 8(4), 381–398. https://doi.org/10.1007/s12193-014-0169-9
   Web of Science ®Google Scholar
• Zheng, X., Liu, Q., Tao, Z., Peng, L., Chen, H., & Tang, Y. (2023). Caregivers’ experiences of caring for non-COVID-19 patients during the pandemic: A qualitative systematic review. Patient Education and Counseling, 115, 107872. https://doi.org/10.1016/j.pec.2023.107872
   PubMed Web of Science ®Google Scholar