Artificial Intelligence for Psychotherapy: A Review of the Current State and Future Directions

Artificial intelligence and psychoanalysis: is it time for psychoanalyst.AI?

The current development of artificial intelligences (AI) is leading to major transformations within society. In this context, we observe how some of these AIs are spontaneously used by individuals as confidants, and even as romantic partners. The emergence of such relationships with AIs raises questions about their integration in psychiatry and the possibility of developing "digital therapists". In this regard, we highlight four key elements (accessibility and availability; confidentiality; knowledge; memory) to compare what an AI offers in comparison to a human therapist. We also discuss the results of the studies that have already investigated the use of such AIs in psychotherapy, particularly in the fields of depression and anxiety. We then propose to reflect more specifically on the possibility of creating a "psychoanalyst.AI," which leads us to examine the elements of the therapeutic relationship (transference, free association, play, dreams, reflexivity, and narrativity) with an AI. In conclusion, we offer some reflections on the relevance of considering AIs as "therapeutic artifact," while taking into account the ethical issues raised by the use of AIs in therapeutic settings.

The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems

BACKGROUND

The increasing demand for psychotherapy and limited access to specialists underscore the potential of artificial intelligence (AI) in mental health care. This study evaluates the effectiveness of the AI-powered Friend chatbot in providing psychological support during crisis situations, compared to traditional psychotherapy.

METHODS

A randomized controlled trial was conducted with 104 women diagnosed with anxiety disorders in active war zones. Participants were randomly assigned to two groups: the experimental group used the Friend chatbot for daily support, while the control group received 60-minute psychotherapy sessions three times a week. Anxiety levels were assessed using the Hamilton Anxiety Rating Scale and Beck Anxiety Inventory. T-tests were used to analyze the results.

RESULTS

Both groups showed significant reductions in anxiety levels. The control group receiving traditional therapy had a 45% reduction on the Hamilton scale and a 50% reduction on the Beck scale, compared to 30% and 35% reductions in the chatbot group. While the chatbot provided accessible, immediate support, traditional therapy proved more effective due to the emotional depth and adaptability provided by human therapists. The chatbot was particularly beneficial in crisis settings where access to therapists was limited, proving its value in scalability and availability. However, its emotional engagement was notably lower compared to in-person therapy.

CONCLUSIONS

The Friend chatbot offers a scalable, cost-effective solution for psychological support, particularly in crisis situations where traditional therapy may not be accessible. Although traditional therapy remains more effective in reducing anxiety, a hybrid model combining AI support with human interaction could optimize mental health care, especially in underserved areas or during emergencies. Further research is needed to improve AI's emotional responsiveness and adaptability.

Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy

Background

Artificial Intelligence (AI) has demonstrated significant potential in transforming psychiatric care by enhancing diagnostic accuracy and therapeutic interventions. Psychiatry faces challenges like overlapping symptoms, subjective diagnostic methods, and personalized treatment requirements. AI, with its advanced data-processing capabilities, offers innovative solutions to these complexities.

Aims

This study systematically reviewed and meta-analyzed the existing literature to evaluate AI's diagnostic accuracy and therapeutic efficacy in psychiatric care, focusing on various psychiatric disorders and AI technologies.

Methods

Adhering to PRISMA guidelines, the study included a comprehensive literature search across multiple databases. Empirical studies investigating AI applications in psychiatry, such as machine learning (ML), deep learning (DL), and hybrid models, were selected based on predefined inclusion criteria. The outcomes of interest were diagnostic accuracy and therapeutic efficacy. Statistical analysis employed fixed- and random-effects models, with subgroup and sensitivity analyses exploring the impact of AI methodologies and study designs.

Results

A total of 14 studies met the inclusion criteria, representing diverse AI applications in diagnosing and treating psychiatric disorders. The pooled diagnostic accuracy was 85% (95% CI: 80%-87%), with ML models achieving the highest accuracy, followed by hybrid and DL models. For therapeutic efficacy, the pooled effect size was 84% (95% CI: 82%-86%), with ML excelling in personalized treatment plans and symptom tracking. Moderate heterogeneity was observed, reflecting variability in study designs and populations. The risk of bias assessment indicated high methodological rigor in most studies, though challenges like algorithmic biases and data quality remain.

Conclusion

AI demonstrates robust diagnostic and therapeutic capabilities in psychiatry, offering a data-driven approach to personalized mental healthcare. Future research should address ethical concerns, standardize methodologies, and explore underrepresented populations to maximize AI's transformative potential in mental health.

Exploring the Potential and Challenges of Digital and AI-Driven Psychotherapy for ADHD, OCD, Schizophrenia, and Substance Use Disorders: A Comprehensive Narrative Review

PURPOSE OF THE REVIEW

Psychotherapy remains a fundamental component of mental health treatment, yet it encounters significant barriers to accessibility and efficacy. The integration of digital therapies, encompassing e-Health, m-Health, and artificial intelligence (AI) offers the transformative potential to enhance therapeutic processes through automated systems that improve access and personalize treatment. This integration, however, involves complex considerations, including health-related quality of life (HRQoL) and the dynamics of the therapeutic alliance. This narrative review synthesizes current literature on the application of digital therapies in psychotherapy, with a focus on attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), schizophrenia, and substance use disorders (SUDs). The review evaluates the effectiveness of these interventions, their impact on HRQoL, therapeutic relationships, and the associated ethical considerations.

COLLECTION AND ANALYSIS OF DATA

Following Green's checklist for narrative reviews, a systematic literature review was conducted on studies published from January 2009 to May 2024. Inclusion criteria centered on digital therapies, including AI-based, Internet-based, and mobile-based interventions in psychotherapy. A total of 23 studies were analyzed.

CONCLUSIONS

The findings indicate that digital therapies hold substantial promise for enhancing therapeutic interventions across various mental health disorders, particularly through chatbots and Internet-based psychotherapy. However, successful integration requires careful attention to the therapeutic alliance, HRQoL, and ethical implications. This review highlights the significant potential of digital therapies, including AI-driven psychotherapy, in managing diverse mental health disorders. A balanced approach to integration, prioritizing patient well-being and addressing ethical complexities, is essential for optimizing treatment outcomes.