Exploration of the dialogue components in Avatar Therapy for schizophrenia patients with refractory auditory hallucinations: A content analysis

Immersive virtual reality in the treatment of auditory hallucinations: A PRISMA scoping review

BACKGROUND

A large group of psychiatric patients suffer from auditory hallucinations (AH) despite relevant treatment regimens. In mental health populations, AH tend to be verbal (AVH) and the content critical or abusive. Trials employing immersive virtual reality (VR) to treat mental health disorders are emerging.

OBJECTIVE

The aim of this scoping review is to provide an overview of clinical trials utilizing VR in the treatment of AH and to document knowledge gaps in the literature.

METHODS

PubMed, Cochrane Library, and Embase were searched for studies reporting on the use of VR to target AH.

RESULTS

16 papers were included in this PRISMA scoping review (ScR). In most studies VR therapy (VRT) was employed to ameliorate treatment resistant AVH in schizophrenia spectrum disorders. Only two studies included patients with a diagnosis of affective disorders. The VRT was carried out with the use of an avatar to represent the patient's most dominant voice.

DISCUSSION

The research field employing VR to treat AH is promising but still in its infancy. Results from larger randomized clinical trials are needed to establish substantial evidence of therapy effectiveness. Additionally, the knowledge base would benefit from more profound qualitative data exploring views of patients and therapists.

Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia

(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients' needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier and logistic regression was conducted over a dataset comprising 162 verbatims of the immersive sessions of 18 patients who previously underwent AT. As a testing dataset, 17 participants, unknown to the dataset, had their first immersive session presented to the combinatory model to predict their clinical outcome. (3) Results: The model accurately predicted the clinical outcome for 15 out of the 17 participants. Classification of the therapeutic interactions achieved an accuracy of 63%. (4) Conclusion: To our knowledge, this is the first attempt to predict the outcome of psychotherapy patients based on the content of their interactions with their therapist. These results are important as they open the door to personalization of psychotherapy based on quantitative information about the interactions taking place during AT.

Unsupervised Machine Learning Driven Analysis of Verbatims of Treatment-Resistant Schizophrenia Patients Having Followed Avatar Therapy

(1) Background: The therapeutic mechanisms underlying psychotherapeutic interventions for individuals with treatment-resistant schizophrenia are mostly unknown. One of these treatment techniques is avatar therapy (AT), in which the patient engages in immersive sessions while interacting with an avatar representing their primary persistent auditory verbal hallucination. The aim of this study was to conduct an unsupervised machine-learning analysis of verbatims of treatment-resistant schizophrenia patients that have followed AT. The second aim of the study was to compare the data clusters obtained from the unsupervised machine-learning analysis with previously conducted qualitative analysis. (2) Methods: A k-means algorithm was performed over the immersive-session verbatims of 18 patients suffering from treatment-resistant schizophrenia who followed AT to cluster interactions of the avatar and the patient. Data were pre-processed using vectorization and data reduction. (3): Results: Three clusters of interactions were identified for the avatar's interactions whereas four clusters were identified for the patient's interactions. (4) Conclusion: This study was the first attempt to conduct unsupervised machine learning on AT and provided a quantitative insight into the inner interactions that take place during immersive sessions. The use of unsupervised machine learning could yield a better understanding of the type of interactions that take place in AT and their clinical implications.

Dyadic Interactions of Treatment-Resistant Schizophrenia Patients Having Followed Virtual Reality Therapy: A Content Analysis

(1) Background: Very little is known about the inner therapeutic processes of psychotherapy interventions for patients suffering from treatment-resistant schizophrenia. Avatar therapy (AT) is one such modalities in which the patient is undergoing immersive sessions in which they interact with an Avatar representing their main persistent auditory verbal hallucination. The aim of this study is to identify the most prevalent dyadic interactions between the patient and the Avatar in AT for patient’s suffering from TRS. (2) Methods: A content analysis of 256 verbatims originating from 32 patients who completed AT between 2017 and 2022 at the Institut universitaire en santé mentale de Montréal was conducted to identify dyadic interactions between the patients and their Avatar. (3) Results: Five key dyads were identified to occur on average more than 10 times for each participant during the immersive sessions across their AT: (Avatar: Reinforcement, Patient: Self-affirmation), (Avatar: Provocation, Patient: Self-affirmation), (Avatar: Coping mechanisms, Patient: Prevention), (Patient: Self-affirmation, Avatar: Reinforcement), and (Patient: Self-appraisal, Avatar: Reinforcement). (4) Conclusion: These dyads offer a first qualitative insight to the interpersonal dynamics and patient-avatar relationships taking place during AT. Future studies on the implication of such dyadic interactions with the therapeutic outcome of AT should be conducted considering the importance of dyadic relationships in psychotherapy.

Application of artificial intelligence tools in diagnosis and treatmentof mental disorders

Introduction: Artificial intelligence research is increasing its application in mental health services. Machine learning, deep learning, semantic analysis in the form of transcriptions of patients' statements enable early diagnosis of psychotic disorders, ADHD, anorexia nervosa. Of great importance are the so-called digital therapists. This paper aims to show the use of AI tools in diagnosing, treating, the benefits and limitations associated with mental disorders. Material and methodS: This literature review was conducted by searching scientific articles from 2015 to 2022. The basis were PubMED, OpenKnowledge, Web of Science, using the following keywords: artificial intelligence, digital therapy, psychiatry, machine learning. Results: A review indicates the widespread use of AI tools in screening for mental disorders. These tools advance the clinical diagnosis medical specialists make up for several years. They impact solving medical staff shortages, lack of access to medical facilities and leveling patient resistance to treatment. The benefits are ultra-fast analysis of large sets of information, effective screening of people in need of specialized psychiatric care, reduction of doctors' duties and maximization of their work efficiency. During the current COVID 19 pandemic, robots in the form of digital psychotherapists are playing a special role. Conclusions: The need for further research, testing and clarification of regulations related to the use of AI tools is indicated. Ethical and social problems need to be resolved. The tools should not form the basis of autonomous therapy without the supervision of highly trained professionals. Human beings should be at the center of analysis just as their health and well-being. Keywords: artificial intelligence, digital therapy, psychiatry, machine learning

Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach

Avatar Therapy (AT) is a modern therapeutic alternative for patients with schizophrenia suffering from persistent auditory verbal hallucinations. Its intrinsic therapeutical process is currently qualitatively analyzed via human coders that annotate session transcripts. This process is time and resource demanding. This creates a need to find potential algorithms that can operate on small datasets and perform such annotations. The first objective of this study is to conduct the automated text classification of interactions in AT and the second objective is to assess if this classification is comparable to the classification done by human coders. A Linear Support Vector Classifier was implemented to perform automated theme classifications on Avatar Therapy session transcripts with the use of a limited dataset with an accuracy of 66.02% and substantial classification agreement of 0.647. These results open the door to additional research such as predicting the outcome of a therapy.

A systematic review of relational-based therapies for the treatment of auditory hallucinations in patients with psychotic disorders

BACKGROUND

Auditory hallucinations in patients with psychotic disorders may be very distressing. Unfortunately, a large proportion of individuals are resistant to pharmacological interventions and the gold-standard cognitive-behavioral therapy for psychosis offers at best modest effects. To improve therapeutic outcomes, several therapies have been created to establish a relationship between voice-hearers and their voices. With increasing literature, we conducted a systematic review of dialogical therapies and examined the evidence behind their efficacy.

METHODS

A systematic search was performed in PubMed, PsycINFO, Web of Science, and Google Scholar. Articles were included if they discussed the effects of dialogical interventions for patients with psychotic disorders.

RESULTS

A total of 17 studies were included within this systematic review. Cumulative evidence from various therapies has shown that entering in a dialog with voices is beneficial to patients, even those who are resistant to current pharmacological treatments. Heightened benefits have been mainly observed with Relating Therapy and Avatar Therapy/Virtual Reality assisted Therapy, with evidence generally of moderate quality. Both these interventions have shown large to very large effects on voices and voice-related distress as well as moderate to large magnitude improvements on affective symptoms. Though, cognitive-behavioral therapy for command hallucinations and making sense of voices noted no improvements on voices.

CONCLUSIONS

Literature on relational-based interventions with a strong emphasis on the relational aspects of voice hearing has shown positive effects. Results suggest that these dialogical therapies might surpass the efficacy of current gold-standard approaches.

Discourse Behavior of Older Adults Interacting With a Dialogue Agent Competent in Multiple Topics

We present a conversational agent designed to provide realistic conversational practice to older adults at risk of isolation or social anxiety, and show the results of a content analysis on a corpus of data collected from experiments with elderly patients interacting with our system. The conversational agent, represented by a virtual avatar, is designed to hold multiple sessions of casual conversation with older adults. Throughout each interaction, the system analyzes the prosodic and nonverbal behavior of users and provides feedback to the user in the form of periodic comments and suggestions on how to improve. Our avatar is unique in its ability to hold natural dialogues on a wide range of everyday topics – 27 topics in three groups, developed in collaboration with a team of gerontologists. The three groups vary in “degrees of intimacy”, and as such in degrees of cognitive difficulty for the user. After collecting data from 9 participants who interacted with the avatar for 7-9 sessions over a period of 3-4 weeks, we present results concerning dialogue behavior and inferred sentiment of the users. Analysis of the dialogues reveals correlations such as greater elaborateness for more difficult topics, increasing elaborateness with successive sessions, stronger sentiments in topics concerned with life goals rather than routine activities, and stronger self-disclosure for more intimate topics. In addition to their intrinsic interest, these results also reflect positively on the sophistication and practical applicability of our dialogue system.

Artificial intelligence and Psychiatry: An overview

The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.

"Like I'm Talking to a Real Person": Exploring the Meaning of Transference for the Use and Design of AI-Based Applications in Psychotherapy

AI-enabled virtual and robot therapy is increasingly being integrated into psychotherapeutic practice, supporting a host of emotional, cognitive, and social processes in the therapeutic encounter. Given the speed of research and development trajectories of AI-enabled applications in psychotherapy and the practice of mental healthcare, it is likely that therapeutic chatbots, avatars, and socially assistive devices will soon translate into clinical applications much more broadly. While AI applications offer many potential opportunities for psychotherapy, they also raise important ethical, social, and clinical questions that have not yet been adequately considered for clinical practice. In this article, we begin to address one of these considerations: the role of transference in the psychotherapeutic relationship. Drawing on Karen Barad’s conceptual approach to theorizing human–non-human relations, we show that the concept of transference is necessarily reconfigured within AI-human psychotherapeutic encounters. This has implications for understanding how AI-driven technologies introduce changes in the field of traditional psychotherapy and other forms of mental healthcare and how this may change clinical psychotherapeutic practice and AI development alike. As more AI-enabled apps and platforms for psychotherapy are developed, it becomes necessary to re-think AI-human interaction as more nuanced and richer than a simple exchange of information between human and nonhuman actors alone.

L’amélioration de la qualité de vie chez les patients atteints d’une schizophrénie réfractaire ayant suivi la Thérapie assistée par la Réalité Virtuelle : une analyse de contenu

Objectifs La schizophrénie, en particulier la schizophrénie résistante aux traitements (SRT), est considérée comme l’un des troubles psychiatriques les plus invalidants en termes d’effets indésirables sur la qualité de vie (QV) des patients. La QV subjective est devenue une cible particulièrement cruciale qui devrait être améliorée avec le traitement, car une QV améliorée peut entraîner le rétablissement chez les patients atteints de schizophrénie. Il existe cependant peu de preuves des effets des interventions psychosociales recommandées sur les mesures non symptomatiques comme la QV. À cet égard, le traitement de la schizophrénie peut être renforcé si, en plus du traitement des symptômes, l’accent thérapeutique est mis sur d’autres sphères importantes pour les patients. Avec les progrès technologiques, la Thérapie assistée par la Réalité Virtuelle (TRV) permet aux entendeurs de voix d’entrer en dialogue direct avec un avatar, animé entièrement par le thérapeute, qui représente leur voix la plus persécutrice. Ceci dans le but de leur permettre d’acquérir un meilleur contrôle sur leurs voix et de travailler sur leur estime de soi. Au-delà de la symptomatologie, les résultats des projets pilotes sur cette thérapie innovante ont montré des résultats significatifs sur la QV.

Méthode Afin de peaufiner les résultats quantitatifs trouvés, cet article a exploré les thèmes émergents d’une analyse de contenu découlant du discours spontané de 10 patients ayant bien répondu à la TRV.

Résultats Quatre thèmes généraux ont émergé : 1) impact de la thérapie sur les voix ; 2) relations interpersonnelles ; 3) bien-être psychologique ; et 4) mode de vie. Cette analyse de contenu a permis d’identifier plusieurs sphères de vie qui sont davantage améliorées chez les patients ayant une SRT à l’aide de la TRV.

Conclusion : La TRV met en évidence l’avenir des approches adaptées aux objectifs des patients qui intègrent plusieurs processus pertinents pour potentiellement améliorer leur QV. La TRV peut avoir des implications potentiellement immenses sur la santé et la qualité de vie des patients. Cette étude fut une première étape vers l’exploration des effets subjectifs de la TRV sur la vie des patients au-delà des symptômes.

Whispers, echoes, friends and fears: forms and functions of voice-hearing in adolescence

BACKGROUND

Despite the high prevalence of voice-hearing in childhood, research with adolescents aged under 16 years is scarce. Theoretical connections between clinical and developmental conceptualizations of voice-hearing are limited, resulting in missed opportunities to explore unusual sensory experiences with young people.

METHODS

Demographic, contextual and qualitative data were collected through a web-based survey with 68 adolescents (M = 14.91; SD = 2.77) from Australia, Canada, Ireland, New Zealand, Spain, the United Kingdom and United States of America. A Foucauldian-informed narrative analysis captured phenomenologically meaningful individual accounts and systemically informed narratives. Analytic layers attended specifically to the form and function of voices, including relational, protective, distressing and nuanced experiences, offering new insights into individual, systemic and cultural interpretative narratives surrounding voice-hearing to inform research, policy and tailored support.

RESULTS

The average self-reported age of onset of voices was 9 years, 5 months. Reciprocal relationships with pleasant voices were evidenced through the narratives and characterization of voices, while distressing voices were described without reciprocity and the voices held greater power over the young person. Positive aspects of negative voices were discussed and are illustrated with a continuum matrix reflecting interpretation and related affect.

CONCLUSIONS

Voice-hearing is a heterogeneous and often complex relational experience for young people, with structural inequalities, relational traumas and social isolation attributed causes of voice-hearing. Developing personal meaning-making mitigated voice-related distress through contextualizing the origin of the voices in past experiences, without attribution to mental illness. Recommendations are proposed for assessment, formulation and relational interventions that recognize the potential impact of the voice-child-other relationship upon psychosocial functioning and wellbeing.

One-year randomized trial comparing virtual reality-assisted therapy to cognitive-behavioral therapy for patients with treatment-resistant schizophrenia

The gold-standard cognitive-behavioral therapy (CBT) for psychosis offers at best modest effects. With advances in technology, virtual reality (VR) therapies for auditory verbal hallucinations (AVH), such as AVATAR therapy (AT) and VR-assisted therapy (VRT), are amid a new wave of relational approaches that may heighten effects. Prior trials have shown greater effects of these therapies on AVH up to a 24-week follow-up. However, no trial has compared them to a recommended active treatment with a 1-year follow-up. We performed a pilot randomized comparative trial evaluating the short- and long-term efficacy of VRT over CBT for patients with treatment-resistant schizophrenia. Patients were randomized to VRT (n = 37) or CBT (n = 37). Clinical assessments were administered before and after each intervention and at follow-up periods up to 12 months. Between and within-group changes in psychiatric symptoms were assessed using linear mixed-effects models. Short-term findings showed that both interventions produced significant improvements in AVH severity and depressive symptoms. Although results did not show a statistically significant superiority of VRT over CBT for AVH, VRT did achieve larger effects particularly on overall AVH (d = 1.080 for VRT and d = 0.555 for CBT). Furthermore, results suggested a superiority of VRT over CBT on affective symptoms. VRT also showed significant results on persecutory beliefs and quality of life. Effects were maintained up to the 1-year follow-up. VRT highlights the future of patient-tailored approaches that may show benefits over generic CBT for voices. A fully powered single-blind randomized controlled trial comparing VRT to CBT is underway.

The therapeutic processes of avatar therapy: A content analysis of the dialogue between treatment-resistant patients with schizophrenia and their avatar

OBJECTIVE

Because the therapeutic processes of Avatar Therapy remain equivocal, the current study aims to further extend our previous findings by analysing the evolution of the avatars' and patients' speech and changes in patient responses as sessions progressed.

DESIGN

Eighteen patients with treatment-resistant schizophrenia were selected from two clinical trials on Avatar Therapy. Three coders analysed both the avatars' and patients' discourse during immersive therapy sessions using content analysis methods.

RESULTS

Our analyses enabled the categorization of the avatar discourse into confrontational techniques (e.g., provocation) and positive techniques (e.g., reinforcement). Patients responded to these utterances using coping mechanism or by expressing emotions, beliefs, self-perceptions or aspirations. Through identification of mutual changes in the interaction between the patient and their avatar, a shift was observed over the sessions from confrontation to a constructive dialogue. Assertiveness, emotional responses and prevention strategies seemed to be central to the therapeutic process, and these usually occur in response to positive techniques.

CONCLUSION

Investigating AT's therapeutic process may help to identify components to achieve positive outcomes and can enable the development of more effective treatments. Further studies should explore the association between these themes and therapeutic response to help predict which patients will better respond to Avatar Therapy.

Young people's narratives of hearing voices: Systemic influences and conceptual challenges

Despite the prevalence of voice hearing in childhood and adolescence, little qualitative research has been undertaken with young people directly to advance phenomenological and aetiological insights into their experiences and interpretations. Consequently, the researchers sought demographic, contextual, and qualitative data from 74 young people from eleven countries, aged 13-18 years (28% = male; 61% = female; 21% = Transgender and Gender Non-Binary [TGNB]), who self-identified as hearing voices. A Foucauldian-informed narrative analysis yielded four analytic chapters, offering novel perspectives into individual, relational, systemic, and cultural interpretative narratives surrounding multisensory and multi-self voice hearing. Overall, young people reported heterogenous experiences of voice hearing and associated sensory experiences, and most participants reported voice hearing beginning between ages 8 and 11. Further, the emotions felt by the child, as well as reactions displayed by people around the child in relation to the voices, influenced voice-related distress and the nature of the voices in a triadic relationship. A continuum of multisensory features of voice content, nature, and relational significance is tentatively proposed to capture the breadth and depth of voice hearing for adolescents to offer a possible framework for future study and intervention design. Specifically, participants described that voice-related distress could be exacerbated by observed anxiety or internalized stigma about voice hearing, social isolation, and attribution to illness. These findings suggest that we may need to reconsider how the experience of hearing voices in childhood influences their relationships and how relationships influence the voice hearing experience. Further, young people seem to have a broad understanding of what the term "hearing voices" means, which could inform how researchers and practitioners work with this group of young people. Finally, participants described benefitting from multisensory coping strategies, such as imagery and meditation, which could offer important considerations for tailoring therapeutic interventions for adolescent voice hearers.

Exploring the Benefits of Virtual Reality-Assisted Therapy Following Cognitive-Behavioral Therapy for Auditory Hallucinations in Patients with Treatment-Resistant Schizophrenia: A Proof of Concept

BACKGROUND

Combining cognitive behavioral therapy (CBT) for psychosis with another psychosocial intervention comprising virtual reality (VR)-assisted therapy (VRT) may improve targeted outcomes in treatment-resistant schizophrenia patients.

METHODS

Ten participants having followed CBT were part of our comparative clinical trial comparing VRT to CBT and were selected at the end of the study as they desired to continue to achieve improvements with VRT (CBT + VRT). Clinical assessments were administered before/after treatments and at follow-ups. Changes in outcomes were examined using linear mixed-effects models. To gain a more in depth understanding on CBT + VRT, therapists' notes, and open interviews on a sub-group of patients were qualitatively analyzed.

RESULTS

Findings showed that the sequence of both interventions was appreciated by all patients. Several significant improvements were found throughout time points on auditory verbal hallucinations, beliefs about voices, depressive symptoms, symptoms of schizophrenia and quality of life. Although most of these improvements were in similar range to those observed in our comparative trial, effects of CBT + VRT on depressive symptoms and symptoms of schizophrenia were larger than those found for either intervention alone.

CONCLUSION

This proof of concept is the first to merge gold-standard CBT with VRT for treatment refractory voices and to suggest a certain synergistic effect.

A typical antipsychotic treatment induced gradually expanding white matter alterations in healthy individuals with persistent auditory verbal hallucinations-an artificially controlled pilot study

OBJECTIVE

The aim of this study was to explore the effects of atypical antipsychotics (AaPs) on brain white matter (WM) tracts in healthy individuals with auditory verbal hallucinations (Hi-AVHs).

METHODS

We analyzed neuroimaging, AVH symptoms, and cognitive assessment data obtained from 39 Hi-AVHs who reported being distressed by persistent AVHs and volunteered to receive AaP treatment. We used tract-based spatial statistics (TBSS) and t tests to explore AaP pharmacotherapy effects on AVH symptoms and brain WM alterations in Hi-AVH subjects.

RESULTS

TBSS and t tests revealed WM alterations after AaP treatment, relative to pretreatment observations. Although AaPs alleviated AVH symptoms, WM alterations in these subjects expanded over 8 months of AaP treatment, encompassing most major WM tracts by the end of the observation period, including the corpus callosum, arcuate fasciculus, cortico-spinal tracts, anterior commissure, and posterior commissure.

CONCLUSIONS

The worsening of AaP-associated WM alterations observed in this study suggest that AaPs may not be a good choice for the treatment of Hi-AVHs despite their ability to alleviate AVHs.

The role of inner speech on the association between childhood adversity and 'hearing voices'

Adverse childhood experiences are associated with later development of psychosis, particularly auditory verbal hallucinations and delusions. Although auditory hallucinations have been proposed to be misattributed inner speech, the relation between childhood adversity and inner speech has not been previously investigated. The first aim was to test whether childhood adversity is associated with inner speech in persons with psychosis. The second aim was to test for the influence of inner speech on the association between childhood adversity and auditory hallucinations. Our final aim was to test for evidence that would falsify the null hypothesis that inner speech does not impact the relationship between childhood adversity and delusions. In persons with psychosis, we found a positive association between childhood adversity and dialogic inner speech. There was a significant total effect of childhood adversity on auditory hallucinations, including an indirect effect of childhood adversity on auditory hallucinations via dialogic inner speech. There was also a significant total effect of childhood adversity on delusions, but no evidence of any indirect effect via inner speech. These findings suggest that childhood adversities are associated with inner speech and psychosis. The relation between childhood adversity and auditory hallucination severity could be partially influenced by dialogic inner speech.

Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy

Background

Research in embodied artificial intelligence (AI) has increasing clinical relevance for therapeutic applications in mental health services. With innovations ranging from ‘virtual psychotherapists’ to social robots in dementia care and autism disorder, to robots for sexual disorders, artificially intelligent virtual and robotic agents are increasingly taking on high-level therapeutic interventions that used to be offered exclusively by highly trained, skilled health professionals. In order to enable responsible clinical implementation, ethical and social implications of the increasing use of embodied AI in mental health need to be identified and addressed.

Objective

This paper assesses the ethical and social implications of translating embodied AI applications into mental health care across the fields of Psychiatry, Psychology and Psychotherapy. Building on this analysis, it develops a set of preliminary recommendations on how to address ethical and social challenges in current and future applications of embodied AI.

Methods

Based on a thematic literature search and established principles of medical ethics, an analysis of the ethical and social aspects of currently embodied AI applications was conducted across the fields of Psychiatry, Psychology, and Psychotherapy. To enable a comprehensive evaluation, the analysis was structured around the following three steps: assessment of potential benefits; analysis of overarching ethical issues and concerns; discussion of specific ethical and social issues of the interventions.

Results

From an ethical perspective, important benefits of embodied AI applications in mental health include new modes of treatment, opportunities to engage hard-to-reach populations, better patient response, and freeing up time for physicians. Overarching ethical issues and concerns include: harm prevention and various questions of data ethics; a lack of guidance on development of AI applications, their clinical integration and training of health professionals; ‘gaps’ in ethical and regulatory frameworks; the potential for misuse including using the technologies to replace established services, thereby potentially exacerbating existing health inequalities. Specific challenges identified and discussed in the application of embodied AI include: matters of risk-assessment, referrals, and supervision; the need to respect and protect patient autonomy; the role of non-human therapy; transparency in the use of algorithms; and specific concerns regarding long-term effects of these applications on understandings of illness and the human condition.

Conclusions

We argue that embodied AI is a promising approach across the field of mental health; however, further research is needed to address the broader ethical and societal concerns of these technologies to negotiate best research and medical practices in innovative mental health care. We conclude by indicating areas of future research and developing recommendations for high-priority areas in need of concrete ethical guidance.