Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Review Article

The Transformative Impact of AI and Machine Learning on Human Psychology

Author(s): Amrita Jyoti, Vikash Yadav*, Amita Pal, Mayur Rahul and Sonu Kumar Jha

Volume 17, Issue 2, 2024

Published on: 05 December, 2023

Article ID: e051223224209 Pages: 9

DOI: 10.2174/0126662558268813231120114051

Price: $65

Abstract

This journal paper examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in shaping human psychology. It investigates how cognitive processes, emotional states, and social interactions are impacted by AI and ML technology. The use of AI and ML in psychology is covered in this study, covering social behaviour analysis, emotion identification, mental health assessment, and personalised therapies. It also explores the moral issues and prospective effects of AI and ML in comprehending and influencing human psychology. This paper emphasises the enormous influence of AI and ML on the comprehension and research of human psychology through a thorough analysis of pertinent literature and empirical evidence. This paper seeks to offer a thorough explanation of the profound effects that AI and ML have had on psychology. We will offer insight into the possible advantages, difficulties, and ethical issues that occur when integrating AI and ML into the study of human psychology by looking at recent developments and implementations of these technologies in psychological research. We will also look at how other areas of psychology, such as cognitive psychology, clinical psychology, social psychology, and neurology, have been impacted by AI and ML.

Graphical Abstract

[1]
P. Khorrami, R.B. Lopes, and R. Chellappa, "Deep learning approaches for facial expression recognition: A comprehensive review", arXiv preprint arXiv:1707.04955,
[2]
N. Sarafianos, S. Petridis, and M. Pantic, "Deep bi-modal regression for apparent emotion recognition", IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 929-942, 2018.
[3]
J.A. Healey, and R.W. Picard, "Detecting stress during real-world driving tasks using physiological sensors", IEEE Trans. Intell. Transp. Syst., vol. 6, no. 2, pp. 156-166, 2005.
[http://dx.doi.org/10.1109/TITS.2005.848368]
[4]
A. Zadeh, P.P. Liang, S. Poria, P. Vij, E. Cambria, and L.P. Morency, "Multimodal emotion recognition in the wild", ACM Trans. Multimed. Comput. Commun. Appl., vol. 14, no. 1, pp. 1-22, 2018.
[5]
G. Gkotsis, A. Oellrich, S. Velupillai, M. Liakata, T.J. Hubbard, and R.J. Dobson, "The language of mental health problems in social media", In: Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA, Association for Computational Linguistics, 2017, pp. 1-10.
[6]
A.N. Vaidyam, H. Wisniewski, J.D. Halamka, and M.S. Kashavan, "Artificial intelligence (AI) applications for the COVID-19 pandemic", Curr. Psychiatry Rep., vol. 22, no. 8, pp. 1-8, 2019.
[7]
J.M. Gómez Penedo, B. Schwartz, J. Giesemann, J.A. Rubel, A.K. Deisenhofer, and W. Lutz, "For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization", Psychother. Res., vol. 32, no. 2, pp. 151-164, 2022.
[http://dx.doi.org/10.1080/10503307.2021.1930242] [PMID: 34034627]
[8]
A. Kautzky, R.J. Baldessarini, and L. Öhlund, "AI applications in mental health care: A literature review", J. Psychiatr. Res., vol. 130, pp. 685-693, 2020.
[9]
K. Huckvale, J. Torous, and M.E. Larsen, "Assessment of the data sharing and privacy practices of smartphone apps for depression and smoking cessation", JAMA Netw. Open, vol. 3, no. 10, pp. e2025108-e2025108, 2020.
[PMID: 31002321]
[10]
T.R. Sahama, R. Hazemi, and S. Aryal, "Artificial intelligence in personalized mental healthcare: How close are we?", IEEE Access, vol. 7, pp. 60488-60501, 2019.
[11]
G.C. Manikis, N.J. Simos, K. Kourou, H. Kondylakis, P. Poikonen-Saksela, K. Mazzocco, R. Pat-Horenczyk, B. Sousa, A.J. Oliveira-Maia, J. Mattson, I. Roziner, C. Marzorati, K. Marias, M. Nuutinen, E. Karademas, and D. Fotiadis, "Personalized risk analysis to improve the psychological resilience of women undergoing treatment for breast cancer: Development of a machine learning–driven clinical decision support tool", J. Med. Internet Res., vol. 25, p. e43838, 2023.
[http://dx.doi.org/10.2196/43838] [PMID: 37307043]
[12]
K.K. Fitzpatrick, A. Darcy, and M. Vierhile, "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 Ment. Health, vol. 4, no. 2, p. e19, 2017.
[http://dx.doi.org/10.2196/mental.7785] [PMID: 28588005]
[13]
K. Muldner, W. Burleson, L. Van der Werff, L. Shen, D. Schwartz, and D. Spruijt-Metz, "Adaptive learning systems: Designing personalized support for students with mental health disabilities", Proceedings of the Fourth (2017) ACM Conference on Learning Scale, pp. 225-228, 2017.
[14]
D.A. Adler, F. Wang, D.C. Mohr, and T. Choudhury, "Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies", PLoS One, vol. 17, no. 4, p. e0266516, 2022.
[http://dx.doi.org/10.1371/journal.pone.0266516] [PMID: 35476787]
[15]
A. Abd-Alrazaq, M. Alajlani, D. Alhuwail, J. Schneider, S. Al-Kuwari, Z. Shah, and M. Househ, "Artificial intelligence–based innovations for mental health care during COVID-19: Scoping review", J. Med. Internet Res., vol. 23, no. 3, p. e20886, 2021.
[PMID: 33600346]
[16]
A. Haines-Delmont, G. Chahal, A.J. Bruen, A. Wall, C.T. Khan, R. Sadashiv, and D. Fearnley, "Testing suicide risk prediction algorithms using phone measurements with patients in acute mental health settings: Feasibility study", JMIR Mhealth Uhealth, vol. 8, no. 6, p. e15901, 2020.
[http://dx.doi.org/10.2196/15901] [PMID: 32442152]
[17]
E.I. Fried, and R.M. Nesse, "Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study", J. Affect. Disord., vol. 172, pp. 96-102, 2015.
[http://dx.doi.org/10.1016/j.jad.2014.10.010] [PMID: 25451401]
[18]
L. Zhang, A. Sadeghian, H. Martirosyan, and J.K. Tsotsos, "Social behavior recognition in continuous video", Proceedings of the European Conference on Computer Vision, pp. 606-623, 2018.
[19]
C.J. Hutto, and E. Gilbert, "Vader: A parsimonious rule-based model for sentiment analysis of social media text", Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, pp. 216-225, 2014.
[http://dx.doi.org/10.1609/icwsm.v8i1.14550]
[20]
S. Abdullah, M. Matthews, E.L. Murnane, G. Gay, and T. Choudhury, "Towards circadian computing: Early to bed and early to rise” makes some of us unhealthy and sleep deprived", In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle Washington, 2014, pp. 673-684
[http://dx.doi.org/10.1145/2632048.2632100]
[21]
S.P. Borgatti, A. Mehra, D.J. Brass, and G. Labianca, "Network analysis in the social sciences", Science, vol. 323, no. 5916, pp. 892-895, 2009.
[http://dx.doi.org/10.1126/science.1165821] [PMID: 19213908]
[22]
N. Cook, A. Mullins, R. Gautam, S. Medi, C. Prince, N. Tyagi, and J. Kommineni, "Evaluating patient experiences in dry eye disease through social media listening research", Ophthalmol. Ther., vol. 8, no. 3, pp. 407-420, 2019.
[http://dx.doi.org/10.1007/s40123-019-0188-4] [PMID: 31161531]
[23]
D. Preotiuc-Pietro, T. Cohn, and L. Ungar, "Modeling individual differences in social behavior", Proc. Natl. Acad. Sci. USA, vol. 116, no. 16, pp. 7756-7765, 2019.
[24]
E. Okon, V. Rachakonda, H.J. Hong, C. Callison-Burch, and J.B. Lipoff, "Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics", J. Am. Acad. Dermatol., vol. 83, no. 3, pp. 803-808, 2020.
[http://dx.doi.org/10.1016/j.jaad.2019.07.014] [PMID: 31306722]
[25]
B.H. Zhang, B. Lemoine, and M. Mitchell, "Mitigating unwanted biases with adversarial learning the 2018 AAAI/ACM Conference,2018",
[http://dx.doi.org/10.1145/3278721.3278779]
[26]
V.S. Raleigh, D. Hussey, I. Seccombe, and R. Qi, "Do associations between staff and inpatient feedback have the potential for improving patient experience? An analysis of surveys in NHS acute trusts in England", Qual. Saf. Health Care, vol. 18, no. 5, pp. 347-354, 2009.
[http://dx.doi.org/10.1136/qshc.2008.028910] [PMID: 19812096]
[27]
M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, and L. Zhang, "Deep learning with differential privacy", Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308-318, .2016,
[http://dx.doi.org/10.1145/2976749.2978318]
[28]
O.H. Salman, Z. Taha, M.Q. Alsabah, Y.S. Hussein, A.S. Mohammed, and M. Aal-Nouman, "A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work", Comput. Methods Programs Biomed., vol. 209, no. 106357, p. 106357, 2021.
[http://dx.doi.org/10.1016/j.cmpb.2021.106357] [PMID: 34438223]
[29]
F. Doshi-Velez, and B. Kim, "Towards a rigorous science of interpretable machine learning", arXiv:1702.08608, 2017,
[30]
M. Srividya, S. Mohanavalli, and N. Bhalaji, "Behavioral modeling for mental health using machine learning algorithms", J. Med. Syst., vol. 42, no. 5, p. 88, 2018.
[http://dx.doi.org/10.1007/s10916-018-0934-5] [PMID: 29610979]
[31]
A. Jobin, M. Ienca, and E. Vayena, "The global landscape of AI ethics guidelines", Nat. Mach. Intell., vol. 1, no. 9, pp. 389-399, 2019.
[http://dx.doi.org/10.1038/s42256-019-0088-2]
[32]
G. Doherty, and D. Coyle, "Chatbots in psychological therapy: A framework for the integration of technology", J. Med. Internet Res., vol. 20, no. 8, p. e228, 2018.
[33]
D.C. Mohr, M. Zhang, and S.M. Schueller, "Personal sensing: Understanding mental health using ubiquitous sensors and machine learning", Annu. Rev. Clin. Psychol., vol. 13, no. 1, pp. 23-47, 2017.
[http://dx.doi.org/10.1146/annurev-clinpsy-032816-044949] [PMID: 28375728]
[34]
S.C. Guntuku, D.B. Yaden, M.L. Kern, L.H. Ungar, and J.C. Eichstaedt, "Detecting depression and mental illness on social media: An integrative review", Curr. Opin. Behav. Sci., vol. 18, pp. 43-49, 2017.
[http://dx.doi.org/10.1016/j.cobeha.2017.07.005]
[35]
J. Torous, M.V. Kiang, J. Lorme, and J.P. Onnela, "New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research", JMIR Ment. Health, vol. 3, no. 2, p. e16, 2016.
[http://dx.doi.org/10.2196/mental.5165] [PMID: 27150677]
[36]
T.R. Wind, M. Rijkeboer, G. Andersson, and H. Riper, "The COVID-19 pandemic: The ‘black swan’ for mental health care and a turning point for e-health", Internet Interv., vol. 20, p. 100317, 2020.
[http://dx.doi.org/10.1016/j.invent.2020.100317] [PMID: 32289019]
[37]
J.M. Gottman, and C.I. Notarius, "Decade review: Observing marital interaction", J. Marriage Fam., vol. 62, no. 4, pp. 927-947, 2000.
[http://dx.doi.org/10.1111/j.1741-3737.2000.00927.x]
[38]
E. Murray, N. Khosla, M.C. Moulson, B.M. Wardecker, L. Wegner, and J. Arnavut, "Emotionally focused couples therapy: Applying technology in psychotherapy", J. Marital Fam. Ther., vol. 44, no. 3, pp. 482-497, 2018.
[39]
S. Duguay, J. Burgess, T. Poell, and M. Zimmer, "The platformization of public discourse", Media Cult. Soc., vol. 41, no. 2, pp. 163-181, 2018.
[40]
J.C. Eichstaedt, H.A. Schwartz, M.L. Kern, G. Park, D.R. Labarthe, R.M. Merchant, S. Jha, M. Agrawal, L.A. Dziurzynski, M. Sap, C. Weeg, E.E. Larson, L.H. Ungar, and M.E.P. Seligman, "Psychological language on Twitter predicts county-level heart disease mortality", Psychol. Sci., vol. 26, no. 2, pp. 159-169, 2015.
[http://dx.doi.org/10.1177/0956797614557867] [PMID: 25605707]
[41]
M. De Choudhury, and E. Kıcıman, "The language of social support in social media and its effect on suicidal ideation risk", Proceedings of the International Conference on Web and Social Media (ICWSM),2017",
[http://dx.doi.org/10.1609/icwsm.v11i1.14891]
[42]
R. Dinga, L. Schmaal, B.W. Penninx, M.J. van Tol, D.J. Veltman, and L. van Velzen, "Evaluating the evidence for biotypes of depression: Methodological replication and extension of Dinga et al. (2019)", Neuroimage, vol. 176, pp. 279-293, 2018.
[43]
A.M. Chekroud, R.J. Zotti, Z. Shehzad, R. Gueorguieva, M.K. Johnson, M.H. Trivedi, T.D. Cannon, J.H. Krystal, and P.R. Corlett, "Cross-trial prediction of treatment outcome in depression: A machine learning approach", Lancet Psychiatry, vol. 3, no. 3, pp. 243-250, 2016.
[http://dx.doi.org/10.1016/S2215-0366(15)00471-X] [PMID: 26803397]
[44]
T.J. Farchione, C.P. Fairholme, K.K. Ellard, C.L. Boisseau, J. Thompson-Hollands, J.R. Carl, M.W. Gallagher, and D.H. Barlow, "Unified protocol for transdiagnostic treatment of emotional disorders: A randomized controlled trial", Behav. Ther., vol. 43, no. 3, pp. 666-678, 2012.
[http://dx.doi.org/10.1016/j.beth.2012.01.001] [PMID: 22697453]
[45]
C. Rauschenbach, L.O. Reis, and L. Castro, "Ethical and social challenges of AI in mental health", Eur. Arch. Psychiatry Clin. Neurosci., vol. 270, no. 2, pp. 139-140, 2020.
[46]
A.L. Seritan, C.S. Haller, and M.M. Adamson, "Challenges in designing AI for psychological assessment", Psychiatr. Ann., vol. 48, no. 7, pp. 339-343, 20202018, .
[47]
R.N. Spreng, K.P. Madore, and D.L. Schacter, "Better living through neuroscience", Nat. Neurosci., vol. 23, no. 12, pp. 1498-1509, 2020.
[48]
D.M. Ruderfer, C.A. Walsh, and S.E. McCarthy, "New approaches to psychiatric genomics and the genetics of mental illness", Neuron, vol. 109, no. 10, pp. 1636-1650, 2021.
[PMID: 33831348]
[49]
T.D. Parsons, and A.A. Rizzo, "Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: A meta-analysis", J. Behav. Ther. Exp. Psychiatry, vol. 61, pp. 147-152, 2018.
[PMID: 17720136]
[50]
B.J. Dietvorst, J.P. Simmons, and C. Massey, "Algorithm aversion: People erroneously avoid algorithms after seeing them err", J. Exp. Psychol. Gen., vol. 149, no. 7, pp. 1140-1154, 2020.
[PMID: 25401381]
[51]
F. Ashley, "The misuse of gender dysphoria: Toward greater conceptual clarity in transgender health", Perspect. Psychol. Sci., vol. 16, no. 6, pp. 1159-1164, 2021.
[http://dx.doi.org/10.1177/1745691619872987] [PMID: 31747342]
[52]
T.R. Insel, "Digital Phenotyping", JAMA, vol. 318, no. 13, pp. 1215-1216, 2017.
[http://dx.doi.org/10.1001/jama.2017.11295] [PMID: 28973224]
[53]
M. Roy, S.J. Minar, P. Dhar, and A.O. Faruq, "Machine learning applications in healthcare: The state of knowledge and future directions", Br J Med Health Res, vol. 10, no. 6, 2023.
[54]
M. Roy, and A.T. Protity, "Hair and scalp disease detection using machine learning and image processing", Eur. J. Inf. Syst., vol. 3, no. 1, pp. 7-13, 2023.
[http://dx.doi.org/10.24018/compute.2023.3.1.85]
[55]
S. Hussain, A. Habib, M.S. Hussain, and A.K. Najmi, "Potential biomarkers for early detection of diabetic kidney disease", Diabetes Res. Clin. Pract., vol. 161, no. 108082, 2020.108082
[http://dx.doi.org/10.1016/j.diabres.2020.108082] [PMID: 32057966]
[56]
L.J. Curtis, R. Valaitis, C. Laur, T. McNicholl, R. Nasser, and H. Keller, "Low food intake in hospital: Patient, institutional, and clinical factors", Appl. Physiol. Nutr. Metab., vol. 43, no. 12, pp. 1239-1246, 2018.
[http://dx.doi.org/10.1139/apnm-2018-0064] [PMID: 29738268]
[57]
C. Iwendi, S. Khan, J.H. Anajemba, A.K. Bashir, and F. Noor, "Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model", IEEE Access, vol. 8, pp. 28462-28474, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2968537]
[58]
H.C. Yi, Z. Ibrahim, and Z. Abu Zaid, "Z. Mat Daud, N.B. Md Yusop, J. Omar, M.N. Mohd Abas, Z. Abdul Rahman, and N. Jamhuri, “Impact of enhanced Recovery after Surgery with preoperative whey protein-infused carbohydrate loading and postoperative early oral feeding among surgical gynecologic cancer patients: An open-labelled randomized controlled trial”", Nutrients, vol. 12, no. 1, p. 264, 2020.
[http://dx.doi.org/10.3390/nu12010264] [PMID: 31968595]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy