An Overview of Artificial Intelligence Applications and Psychology

Tahan, Mohammad

doi:10.32598/ajnpp.5.1.3

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Ten of the 27 excluded studies either tried to predict the outcome of cognitive behavior therapy by using natural language processing, or tried to predict optimal therapeutic interventions with sociodemographic data (30-40). Another 16 studies excluded were reviews about machine learning and its utilization within psychotherapy, psychopharmacotherapy, and diagnostics (19,27,38,(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51). Another 8 Studies were excluded because they treated patients with psychotherapy, yet did not specify which kind of treatment approach they used (22, 29, 52-57).…”

Section: Study Selectionmentioning

confidence: 99%

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

et al. 2023

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IntroductionAlthough outpatient psychodynamic psychotherapy is effective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient's needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives.MethodsFor this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.ResultsIn total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021.DiscussionWe conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems.

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Section: Study Selectionmentioning

confidence: 99%

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

et al. 2023

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“…The literature on artificial intelligence supports that there are several applications of specialized tools and techniques that could be employed to enhance psychotherapy [19].…”

Section: Artificial Intelligence In the Field Of Psychotherapymentioning

confidence: 99%

Deciphering the Mosaic of Therapeutic Potential: A Scoping Review of Neural Network Applications in Psychotherapy Enhancements

Hudon,

Aird,

La Haye-Caty

2023

BioMedInformatics

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Background: Psychotherapy is a component of the therapeutic options accessible in mental health. Along with psychotherapy techniques and indications, there is a body of studies on what are known as psychotherapy’s common factors. However, up to 40% of patients do not respond to therapy. Artificial intelligence approaches are hoped to enhance this and with the growing body of evidence of the use of neural networks (NNs) in other areas of medicine, this domain is lacking in the field of psychotherapy. This study aims to identify the different uses of NNs in the field of psychotherapy. Methods: A scoping review was conducted in the electronic databases EMBASE, MEDLINE, APA, and CINAHL. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement influenced this study’s design. Studies were included if they applied a neural network algorithm in the context of a psychotherapeutic approach. Results: A total of 157 studies were screened for eligibility, of which 32 were fully assessed. Finally, eight articles were analyzed, and three uses were identified: predicting the therapeutic outcomes, content analysis, and automated categorization of psychotherapeutic interactions. Conclusions: Uses of NNs were identified with limited evidence of their effects. The potential implications of these uses could assist the therapist in providing a more personalized therapeutic approach to their patients. Given the paucity of literature, this study provides a path for future research to better understand the efficacy of such uses.

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“…However, human behaviour itself cannot be copied in totality by technology. 10 A similar review in Canada, that addressed implementation of AI in healthcare, showed that machines do not replace healthcare providers since it is not about making better decisions, rather about collecting and having better information. Therefore, maximising the healthcare benefits can be through reaching the maximum potential of combining both the human and AI technology.…”

Section: How Has Artificial Intelligence Contributed To Healthcare Provision Benefits?mentioning

confidence: 99%

The Healthcare Benefits and Impact of Artificial Intelligence Applications on Behaviour of Healthcare Users: A Structured Review of Primary Literature

Lutfi

Glasauer

Spittler

2020

J Int Soc Telemed eHealth

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Introduction: Artificial intelligence (AI) is one of the most considered topics of the current time. AI has the power to bring revolutionary improvements to the world of technology not only in the field of computer science but also in other fields like medical sciences. Objectives: This paper assumes the adoption of appropriate AI engineering principals in previous studies, and focusses on providing a structured review of the impact of AI on human society and the individual human being as a technology user. Additionally, it opens a window on how the future will look like in terms of AI and personalised medicine. Methods: The paper employed a qualitative research approach and data were collected through a structured literature review. Twenty-three peer reviewed papers were identified and analysed in relation to their relevance to the study. Results: Previous studies show a positive impact on users' behaviour is expected in supporting their healthcare needs especially in decision-making, personalised treatment and future diseases prediction, and that integrating users in studying AI impact is essential to test possible implications of the technology. Conclusion: Results indicate that without a clear understanding of why patients need AI, or how AI can support individuals with their healthcare needs, it is difficult to visualise the kinds of AI applications that have a meaningful and sustainable impact the daily lives of individuals. Therefore, there is an emerging need to understand the impact of AI technology on users' behaviour to maximise the potential benefits of AI technology.

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An Overview of Artificial Intelligence Applications and Psychology

Cited by 4 publications

References 17 publications

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

Deciphering the Mosaic of Therapeutic Potential: A Scoping Review of Neural Network Applications in Psychotherapy Enhancements

The Healthcare Benefits and Impact of Artificial Intelligence Applications on Behaviour of Healthcare Users: A Structured Review of Primary Literature

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