The role of artificial intelligence in Clinical Psychological Assessment an analytical approach between clinical accuracy and ethical challenges
Keywords:
artificial intelligence, psychological assessment , clinical accuracy, ethical challenges
Abstract
This article explores the use of artificial intelligence in clinical psychological assessment, focusing on its benefits and limitations. It highlights how AI can enhance diagnostic accuracy, efficiency, and reliability, while also raising ethical concerns related to confidentiality, professional responsibility, and the human aspect of therapy.
Based on an analytical review of recent research, the study shows that although AI supports clinical decision-making, it cannot replace human judgment, especially in complex emotional or cultural contexts. The article concludes by stressing the need for ethically regulated, hybrid assessment models that integrate artificial intelligence with clinical expertise to preserve the quality and humanity of psychological practice.
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References
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-Moses, A. (2018). Psychological assessment in clinical practice. Cairo: University Publishing House.
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-Obermeyer, Z., et al. (2019). Racial bias in health algorithms. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
-Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
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-Al-Rabeei, F. (2021). Artificial intelligence and mental health. Beirut: Center for Social Intelligence Studies.
-Al-Rabeei, F. (2021). Artificial intelligence in psychology: An introduction to clinical applications. Riyadh: Academic Publishing House.
-Al-Rabeei, F. (2021). Bias in psychological algorithms: Monitoring and analysis. Beirut: Center for Social Intelligence Studies.
-Al-Sarjani, H. (2014). Introduction to clinical psychology. Cairo: House of University Knowledge.
-Al-Sarraj, P. (2017). Introduction to psychological assessment methods. Amman: Academic Book House.
-Al-Sarraj, P. (2020). Ethical guidelines in the use of artificial intelligence in psychiatry. Journal of Medical Ethics, 5(2), 101–120.
-Al-Sarraj, P. (2021). Artificial intelligence tools in mental health assessment: Towards a digital understanding of disorders. Riyadh: Dar Al-Qalam.
-Al-Sarraj, P. (2021). Determinants of psychological assessment in the age of algorithms. Riyadh: University Publishing House.
-Al-Zahrani, I. (2021). Artificial intelligence legislation: A legal and rights perspective. Riyadh: University Publishing House.
-Al-Zahrani, I. (2021). Psychological legislation in the face of artificial intelligence: An analytical reading. Arab Journal of Psychiatry and Technology, 5(1), 45–62.
-Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
-APA (2017). Ethical Principles of Psychologists and Code of Conduct. American Psychological Association.
-Ben Taleb, H. (2022). The effectiveness of artificial intelligence applications in supporting mental health among rural university students. Arab Journal of Psychological Technology, 6(1), 112–130.
-Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3287560.3287598
-Butcher, J. N., et al. (2001). Minnesota Multiphasic Personality Inventory (MMPI-2) Manual. University of Minnesota Press.
-Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517–518. https://doi.org/10.1001/jama.2017.7797
-Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media. ACM Transactions on Computer-Human Interaction, 27(5), 1–53. https://doi.org/10.1145/3369941
-Chaudhury, S., Putchala, S. R., & Sinha, R. (2021). Artificial Intelligence in Psychiatry: A Clinical Perspective. Indian Journal of Psychiatry, 63(2), 113–118. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_596_20
-Coppersmith, G., Dredze, M., & Harman, C. (2015). Quantifying mental health signals in Twitter. In ACL Workshop on Computational Linguistics and Clinical Psychology. https://doi.org/10.3115/v1/W15-1204
-Cummins, N., et al. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10–49. https://doi.org/10.1016/j.specom.2015.03.004
-Elhai, J. D., & Ford, J. D. (2011). Advances in clinical decision-making in mental health care. Psychological Assessment, 23(3), 603–610. https://doi.org/10.1037/a0023451
-El-Shenawy, A. (2020). An introduction to the analysis of natural language in psychological contexts. Cairo: Arab Center for Research and Training.
-El-Shenawy, A. (2020). Artificial intelligence and its applications in digital psychotherapy. Cairo: The House of Arab Thought.
Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
-Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy using a conversational agent. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
-Floridi, L., et al. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
-Gaffney, H., Mansell, W., & Tai, S. (2019). Conversational agents in mental health: Systematic review. JMIR Mental Health, 6(10), e14166. https://doi.org/10.2196/14166
-Girard, J. M., et al. (2014). Social risk and depression. In Proceedings of the ACM Conference. https://doi.org/10.1145/2632048.2636074
-Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
-Goodman, B., & Flaxman, S. (2017). EU regulations on algorithmic decision-making. AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741
-Groves, M. (2019). Clinical decision making in mental health. Journal of Clinical Psychology, 75(2), 145–159. https://doi.org/10.1002/jclp.22729
-Haines, N., et al. (2019). Automated facial coding using machine learning. PLOS ONE, 14(2), e0211735. https://doi.org/10.1371/journal.pone.0211735
-Hunsley, J., & Mash, E. J. (2017). A guide to assessment scales in clinical psychology. Oxford University Press.
-Insel, T. (2018). The future of mental health care. Harvard University Conference Paper.
-Kanfer, F. H., & Goldstein, A. P. (2013). Behavioral approaches to assessment. Routledge.
-Larsen, M. E., et al. (2019). Smartphone tools for suicide prevention. PLOS ONE, 11(4), e0152285. https://doi.org/10.1371/journal.pone.0152285
-Low, D. M., et al. (2020). Automated assessment using speech. Laryngoscope Investigative Otolaryngology, 5(1), 96–116. https://doi.org/10.1002/lio2.354
-Ma, T., et al. (2022). Limits of algorithmic mental health care. AI & Society, 37(3), 1127–1140. https://doi.org/10.1007/s00146-021-01266-3
-Miller, L. (2016). Psychological assessment. Springer.
-Miner, A. S., et al. (2020). Digital psychiatry and NLP. Current Opinion in Behavioral Sciences, 36, 89–94. https://doi.org/10.1016/j.cobeha.2020.08.001
-Mitchell, M. (1998). An introduction to genetic algorithms. MIT Press.
-Mittelstadt, B. D., et al. (2016). The ethics of algorithms. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
-Moses, A. (2018). Psychological assessment in clinical practice. Cairo: University Publishing House.
Muench, F., et al. (2016). Biological bases of psychological disorders. Academic Press.
-O’Neil, C. (2016). Weapons of Math Destruction. Crown Publishing.
-Obermeyer, Z., et al. (2019). Racial bias in health algorithms. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
-Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
-Sullivan, H. S., & Reitz, M. (2020). The Rorschach Test. American Journal of Psychotherapy.
-Tadesse, M. M., et al. (2019). Depression detection on Reddit. IEEE Access, 7, 44883–44893. https://doi.org/10.1109/ACCESS.2019.2909180
-Topol, E. (2019). Deep Medicine. Basic Books.
-Torous, J., & Roberts, L. W. (2017). Ethical use of mobile health tech. Journal of Nervous and Mental Disease, 205(1), 4–8. https://doi.org/10.1097/NMD.0000000000000596
-Torous, J., et al. (2018). Mental health apps usage. JMIR Mental Health, 5(1), e11715. https://doi.org/10.2196/11715
-Trifan, A., et al. (2021). Passive sensing via smartphones. JMIR mHealth and uHealth, 9(2), e17528. https://doi.org/10.2196/17528
-Young, S. H., et al. (2015). Psychological testing and evaluation. Wiley.
-Zerilli, J., et al. (2019). Algorithmic decision-making. Minds and Machines, 29(4), 555–578. https://doi.org/10.1007/s11023-019-09508-w
-Zhou, Y., et al. (2020). Detecting mental health disorders on social media. ACM Transactions on the Web, 14((3), 1–30. https://doi.org/10.1145/3394171
Published
2026-04-17
How to Cite
Sider, K., & Qouta, S. (2026). The role of artificial intelligence in Clinical Psychological Assessment an analytical approach between clinical accuracy and ethical challenges. Journal of Science and Knowledge Horizons, 6(1), 163-184. https://doi.org/10.34118/jskp.v6i1.4560
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
