Exploring the cyber complexity and cyberpsychology of the internet of things and AI tools in healthcare organizations
DOI:
https://doi.org/10.34140/bjbv7n4-079Palavras-chave:
artificial intelligence, AI in therapy, mobile health applications, health technology, mental health therapy, counseling ethics, artificial intelligence oversight, human-computer interactionResumo
The integration of artificial intelligence (AI) into healthcare and mental health systems through advanced into 5G communication networks offers significant promise but also profound challenges. AI models are increasingly used to manage large data flows, optimize decision-making, and enable real-time applications such as telemedicine, remote monitoring, and mental health support tools. These technologies have the potential to expand access to care, reduce resource burdens, and enhance efficiency. However, without changes to current processes of development, deployment, reliance, and oversight, they risk reinforcing inequities, eroding trust, and compromising patient safety in healthcare businesses and organizations.
The risks are particularly acute in mental health, where algorithmic blind spots can misinterpret cultural expressions of distress, fail to detect crises, or create over-reliance on systems that simulate empathy without the capacity for genuine care. In healthcare delivery more broadly, AI-enabled tools can privilege certain populations, exacerbate disparities, or expose patients to data misuse in the absence of robust safeguards. These challenges highlight the urgent need for comprehensive governance frameworks, transparent reporting, strong privacy protections, and meaningful stakeholder engagement that includes clinicians, patients, policymakers, and underrepresented communities.
This paper argues that AI should be viewed not as a substitute for human expertise but as an augmentative tool that supports ethical, safe, and equitable care. By embedding ethics and safety into design, deployment, and oversight processes, and by engaging diverse voices in decision-making, healthcare systems can ensure that AI strengthens rather than destabilizes therapeutic environments and contributes to the development of trustworthy, inclusive, and resilient healthcare infrastructures.
Referências
Ademola, O. E. (2024). Detailing the stakeholder theory of management in the AI world: A position paper on ethical decision-making. Social Informatics, Business, Politics, Law, Environmental Sciences & Technology Journal, 10(1), 1-8.
Albikawi, Z., Abuadas, M., & Rayani, A. M. (2025, May). Nursing Students’ Perceptions of AI-Driven Mental Health Support and Its Relationship with Anxiety, Depression, and Seeking Professional Psychological Help: Transitioning from Traditional Counseling to Digital Support. In Healthcare (Vol. 13, No. 9, p. 1089). MDPI.
Alfateh, M. A. A., Messaadia, M., & Ali, M. (2023, September). Exploring the dynamics of team formation in human-artificial intelligence collaboration. In the 2023 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 384-388). IEEE.
Amsari D, Wahyuni E, Fadhilaturrahmi F. The Social Learning Theory: Albert Bandura for Elementary School Students. J Basicedu. 2024;8(2):1654–62. https://doi.org/10.31004/BASICEDU.V8I2.7247
Baulcomb, J. S. (2003). Management of Change through Force Field Analysis. Journal of nursing management, 11(4), 275-280.
Chen J, Xia S, Lin T. A framework of moderators in social norm-based message persuasiveness based on a systematic review. Hum Commun Res. 2024;50(2):285–98. https://doi.org/10.1093/HCR/HQAD043
Chen Y, Li X. Expectancy Violations and Discontinuance Behavior in Live-Streaming Commerce: Exploring Human Interactions with Virtual Streamers. Behav Sci. 2024;14(10):920. https://doi.org/10.3390/BS14100920
da Costa, R. L., Gonçalves, R., & Montez, A. (2023). Artificial intelligence applied to stakeholder theory. In Digital Technologies and Transformation in Business, Industry and Organizations: Volume 2 (pp. 101-125). Cham: Springer Nature Switzerland.
Evans K, O’Donnell K. Relational Cultural Theory and Intervention Approaches with Adolescent Girls: An Integrative Review. Affilia. 2025;40(1):83–102. https://doi.org/10.1177/08861099241271296
Ganesh M, S., & N, V. (2025). Artificial Intelligence (AI) Generated Health Counseling For Mental Illness Patients. Current Psychiatry Research and Reviews, 21(3), 269-283.
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.
Hagemann, M., & Cechlovsky, S. (2024). Revisiting the change curve: A rigorous examination and three case studies prompting a re-evaluation of a timeless concept. Human Systems Management, 43(6), 1048-1061.
Horstkötter D, Kanne M, Karbouniaris S, Lazrak N, Bulgheroni M, Sheltawy E, et al. Decision-making on an AI-supported youth mental health app: A multilogue among ethicists, social scientists, AI-researchers, biomedical engineers, young experiential experts, and psychiatrists. J Responsible Technol. 2025;22:100119. https://doi.org/10.1016/J.JRT.2025.100119
Isaacs WN. Toward an Action Theory of Dialogue. Int J Public Adm. 2001;24(7–8):709–48. https://doi.org/10.1081/PAD-100104771
Issar S. The Social Construction of Algorithms in Everyday Life: Examining TikTok Users’ Understanding of the Platform’s Algorithm. Int J Hum Comput Interact. 2024;40(18):5384–98. https://doi.org/10.1080/10447318.2023.2233138
Kaelin, V. C., Tewari, M., Benouar, S., & Lindgren, H. (2024). Developing Teamwork: Transitioning Between Stages in Human-Agent Collaboration. Frontiers in Computer Science, 6, 1455903.
Koffmann A. Early Distress Score Instability Predicts Outcome in Brief Psychotherapy. J Couns Psychol. 2023;70(5):595–604. https://doi.org/10.1037/COU0000683
Matthews, M. J., Su, R., Yonish, L., McClean, S., Koopman, J., & Yam, K. C. (2025). A review of artificial intelligence, algorithms, and robots through the lens of stakeholder theory. Journal of Management, 51(6), 2627-2676.
Murad RJ. Investigating The Impact of Digital Technology on Adolescent Identity Formation on Selected Students in SAIS: A Psychological Approach. Int J Innov Sci Res Technol. 2024; 2726–36. https://doi.org/10.38124/IJISRT/IJISRT24APR1823
Ningrum, C. A., & Ramansyah, F. I. (2024). The Influence Artificial Intelligence on Mental Health in the Digital Era and Virtual Counseling Services. BICC Proceedings, 2, 176-182.
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, 3, 100099.
Paganelli, M. Identity Construction and Representation in the Digital Age: A Gen Z Social Media Perspective. 2024. https://doi.org/10.13140/RG.2.2.13073.95846
Park, G., Chung, J., & Lee, S. (2023). Effect of AI chatbot emotional disclosure on user satisfaction and reuse intention for mental health counseling: A serial mediation model. Current Psychology, 42(32), 28663-28673.
Ping, Y. (2024). Experience in psychological counseling supported by artificial intelligence technology. Technology and Health Care, 32(6), 3871-3888.
Pu I, Ravi P, Dinh LD, Joe C, Ogoe C, Li Z, et al. “How can we learn and use AI at the same time?”: Participatory Design of GenAI with High School Students. 2025:204–20. https://doi.org/10.1145/3713043.3727057
Rehman, F., & Sajjad, S. (2025). Bridging Technology and Therapy: Exploring AI in Mental Health Services through Counselors' and Students' Perspectives. Online Media and Society, 6(1), 31-44.
Rheu M, Dai Y, Meng J, Peng W. When a Chatbot Disappoints You: Expectancy Violation in Human-Chatbot Interaction in a Social Support Context. Commun Res. 2024;51(7):782–814. https://doi.org/10.1177/00936502231221669
Rose T, Lambert S, Liu C, Raghunathan RS, Musci RJ, Sullivan ADW, et al. Socio‐ecological domains and adolescent mental health: An application of the dual‐factor model. J Res Adolesc. 2025;35(2):e70029. https://doi.org/10.1111/JORA.70029
Ryan T, Ryan N, Hynes B. The integration of human and non-human actors to advance healthcare delivery: unpacking the role of actor-network theory, a systematic literature review. BMC Health Serv Res. 2024;24(1):1–33. https://doi.org/10.1186/S12913-024-11866-4
Scharp KM. Relational Dialectics Theory. Oxf Res Encycl Commun. 2023. https://doi.org/10.1093/ACREFORE/9780190228613.013.1456
Swanson, D. J., & Creed, A. S. (2014). Sharpening the focus of force field analysis. Journal of Change Management, 14(1), 28-47.
Truong, V. (2023, October). Understanding Opinions and Emotions of Generative Artificial Intelligence Assets Using the Gartner Hype Cycle and the Kübler-Ross Change Curve. In World Congress on Engineering Asset Management (pp. 13-30). Cham: Springer Nature Switzerland.
Tsagem, S. Y., & Abdullahi, R. (2024). Artificial Intelligence in Mental Health Counseling: History, Innovations, Ethics, and Future Prospects.
Zou W, Yang X, Liu Z. Scientific tool or torture device? A relational dialectics theory approach to the Speculum’s symbolism in China. J Soc Pers Relatsh. 2025. https://doi.org/10.1177/02654075251340266
Chiat J, Ong L, Ning Y, Liu M, Ma Y, Liang Z, et al. Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action. 2025. https://www.arxiv.org/pdf/2502.07794
Cho Y, Kim H, Park JH. Augmented teletherapy: Integrating AI as clinical decision support in digital mental health. NPJ Digit Med. 2023;6(1):105. https://doi.org/10.1038/s41746-023-00871-x
Donia J, Shaw JA. Co-design and ethical artificial intelligence for health: An agenda for critical research and practice. Big Data Soc. 2021;8(2). https://doi.org/10.1177/205395172110652
Gomez L, Mendes P, Cruz S. Embedding clinical ethics in LLMs: Algorithmic transparency and therapeutic fidelity. AI Ethics. 2025;6:34–52. https://doi.org/10.1007/s43681-024-00219-w
Hanss K, Sarma KV, Glowinski AL, Krystal A, Saunders R, Halls A, et al. Assessing the Accuracy and Reliability of Large Language Models in Psychiatry Using Standardized Multiple-Choice Questions: Cross-Sectional Study. J Med Internet Res. 2025;27(1):e69910. https://doi.org/10.2196/69910
Heston TF. Safety of Large Language Models in Addressing Depression. Cureus. 2023;15(12):e50729. https://doi.org/10.7759/CUREUS.50729
Jiang Q, Zhang L, Vellido A. Evaluating safety and bias in large language models applied to mental health: A simulation study. J Med Internet Res. 2023;25:e48276. https://doi.org/10.2196/48276
Joseph AP, Babu A. Transference and the psychological interplay in AI-enhanced mental healthcare. Front Psychiatry. 2024;15. https://doi.org/10.7759/cureus.50729
Li H, Shah A, Banerjee A. Data privacy concerns in AI mental health platforms: A comparative study of GDPR and HIPAA compliance. Health Technol. 2023;13(4):987–96. https://doi.org/10.1007/s12553-023-00765-0
Li T, Yang S, Wu J, Wei J, Hu L, Li M, et al. Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation. 2025. https://arxiv.org/pdf/2502.17899
Ohu FC, Jones LA. The intersection of cyberwarfare, social media, and adolescent self-esteem: a forensic cyberpsychology analysis. In: Proceedings of the International Conference of Cyberwarfare and Security (ICCWS). 2025.
Ojewale V, Steed R, Vecchione B, Birhane A, Raji ID. Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling. In: Proceedings of the Conference on Human Factors in Computing Systems. 2025. https://doi.org/10.1145/3706598.3713301
Park SH, An J, Kim D. Legal ambiguity and user misperception in AI-based mental health chatbots: A content and policy analysis. Health Policy Technol. 2024;13(2):100803. https://doi.org/10.1016/j.hlpt.2024.100803
Prichett LM, Yolken RH, Severance EG, Carmichael D, Zeng Y, Lu Y, et al. COVID-19 and Youth Mental Health Disparities: Intersectional Trends in Depression, Anxiety, and Suicide Risk-Related Diagnoses. Acad Pediatr. 2024;24(5):837–47. https://doi.org/10.1016/J.ACAP.2024.01.021
Racine N, McArthur BA, Cooke JE, Eirich R, Zhu J, Madigan S. Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19: A Meta-analysis. JAMA Pediatr. 2021;175(11):1142–50. https://doi.org/10.1001/jamapediatrics.2021.2482
Thomas MA, Okafor B, Dlamini M. Cultural competence in AI mental health tools: Risks of algorithmic imperialism in low-resource settings. Glob Health Action. 2024;17(1):2294134. https://doi.org/10.1080/16549716.2024.2294134
Venerito V, Gupta L, Mileto S, Iannone F, Bilgin E. Ethical challenges and regulatory pathways for artificial intelligence in rheumatology. Rheumatol Adv Pract. 2025;9(2):35. https://doi.org/10.1093/RAP/RKAF035
Villegas-Galaviz C, Martin K. Moral distance, AI, and the ethics of care. AI Soc. 2024;39:1695–706. https://doi.org/10.1007/s00146-023-01642-z
Vos T, Horne R, Kyu HH. Global Burden of Mental Disorders: GBD 2023 Update. Lancet Public Health. 2023;8(9):e646–54. https://doi.org/10.1016/S2468-2667(23)00151-0
Weiner EB, Dankwa-Mullan I, Nelson WA, Hassanpour S. Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digit Health. 2025;4(4):e0000810. https://doi.org/10.1371/journal.pdig.0000810
World Health Organization (WHO). Child and Adolescent Mental and Brain Health. 2025. https://www.who.int/activities/improving-the-mental-and-brain-health-of-children-and-adolescents
Yang X, Liu Z, Gao L. Performance evaluation of GPT-4 and Med-PaLM 2 in psychotherapeutic tasks. Front Psychiatry. 2024;15:1223456. https://doi.org/10.3389/fpsyt.2024.1223456
