Key Considerations for Incorporating Conversational AI in Psychotherapy

Effectiveness of a Mental Health Chatbot for People With Chronic Diseases: Randomized Controlled Trial

BACKGROUND

People with chronic diseases tend to experience more mental health issues than their peers without these health conditions. Mental health chatbots offer a potential source of mental health support for people with chronic diseases.

OBJECTIVE

The aim of this study was to determine whether a mental health chatbot can improve mental health in people with chronic diseases. We focused on 2 chronic diseases in particular: arthritis and diabetes.

METHODS

Individuals with arthritis or diabetes were recruited using various web-based methods. Participants were randomly assigned to 1 of 2 groups. Those in the treatment group used a mental health chatbot app (Wysa [Wysa Inc]) over a period of 4 weeks. Those in the control group received no intervention. Participants completed measures of depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder Scale-7), and stress (Perceived Stress Scale-10) at baseline, with follow-up testing 2 and 4 weeks later. Participants in the treatment group completed feedback questions on their experiences with the app at the final assessment point.

RESULTS

A total of 68 participants (n=47, 69% women; mean age 42.87, SD 11.27 years) were included in the analysis. Participants were divided evenly between the treatment and control groups. Those in the treatment group reported decreases in depression (P<.001) and anxiety (P<.001) severity over the study period. No such changes were found among participants in the control group. No changes in stress were reported by participants in either group. Participants with arthritis reported higher levels of depression (P=.004) and anxiety (P=.004) severity than participants with diabetes over the course of the study, as well as higher levels of stress (P=.01); otherwise, patterns of results were similar across these health conditions. In response to the feedback questions, participants in the treatment group said that they liked many of the functions and features of the app, the general design of the app, and the user experience. They also disliked some aspects of the app, with most of these reports focusing on the chatbot's conversational abilities.

CONCLUSIONS

The results of this study suggest that mental health chatbots can be an effective source of mental health support for people with chronic diseases such as arthritis and diabetes. Although cost-effective and accessible, these programs have limitations and may not be well suited for all individuals.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04620668; https://www.clinicaltrials.gov/study/NCT04620668.

Depiction of conversational agents as health professionals: a scoping review

OBJECTIVE

The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care.

INTRODUCTION

The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings.

INCLUSION CRITERIA

This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors.

METHODS

This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables.

RESULTS

A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents.

CONCLUSIONS

The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.

Artificial intelligence in positive mental health: a narrative review

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

Assessing the potential of ChatGPT for psychodynamic formulations in psychiatry: An exploratory study

Although there were several attempts to apply ChatGPT (Generative Pre-Trained Transformer) to medicine, little is known about therapeutic applications in psychiatry. In this exploratory study, we aimed to evaluate the characteristics and appropriateness of the psychodynamic formulations created by ChatGPT. Along with a case selected from the psychoanalytic literature, input prompts were designed to include different levels of background knowledge. These included naïve prompts, keywords created by ChatGPT, keywords created by psychiatrists, and psychodynamic concepts from the literature. The psychodynamic formulations generated from the different prompts were evaluated by five psychiatrists from different institutions. We next conducted further tests in which instructions on the use of different psychodynamic models were added to the input prompts. The models used were ego psychology, self-psychology, and object relations. The results from naïve prompts and psychodynamic concepts were rated as appropriate by most raters. The psychodynamic concept prompt output was rated the highest. Interrater agreement was statistically significant. The results from the tests using instructions in different psychoanalytic theories were also rated as appropriate by most raters. They included key elements of the psychodynamic formulation and suggested interpretations similar to the literature. These findings suggest potential of ChatGPT for use in psychiatry.

AI Chatbots in Clinical Laboratory Medicine: Foundations and Trends

BACKGROUND

Artificial intelligence (AI) conversational agents, or chatbots, are computer programs designed to simulate human conversations using natural language processing. They offer diverse functions and applications across an expanding range of healthcare domains. However, their roles in laboratory medicine remain unclear, as their accuracy, repeatability, and ability to interpret complex laboratory data have yet to be rigorously evaluated.

CONTENT

This review provides an overview of the history of chatbots, two major chatbot development approaches, and their respective advantages and limitations. We discuss the capabilities and potential applications of chatbots in healthcare, focusing on the laboratory medicine field. Recent evaluations of chatbot performance are presented, with a special emphasis on large language models such as the Chat Generative Pre-trained Transformer in response to laboratory medicine questions across different categories, such as medical knowledge, laboratory operations, regulations, and interpretation of laboratory results as related to clinical context. We analyze the causes of chatbots' limitations and suggest research directions for developing more accurate, reliable, and manageable chatbots for applications in laboratory medicine.

SUMMARY

Chatbots, which are rapidly evolving AI applications, hold tremendous potential to improve medical education, provide timely responses to clinical inquiries concerning laboratory tests, assist in interpreting laboratory results, and facilitate communication among patients, physicians, and laboratorians. Nevertheless, users should be vigilant of existing chatbots' limitations, such as misinformation, inconsistencies, and lack of human-like reasoning abilities. To be effectively used in laboratory medicine, chatbots must undergo extensive training on rigorously validated medical knowledge and be thoroughly evaluated against standard clinical practice.

Natural language processing for mental health interventions: a systematic review and research framework

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

The oracle of Delphi 2.0: considering artificial intelligence as a challenging tool for the treatment of eating disorders

In this editorial, we discuss how the diffusion of Artificial Intelligence (AI)-based tools-such as the recently available conversational AIs-could impact and transform eating disorders (EDs) care. We try to envision the possible use of AI by individuals affected by EDs and by clinicians, in terms of prevention, support to treatment, and development of new and actually personalized treatment strategies. We then focus on how the introduction of AI into psychotherapy could either represent an element of disruption for the therapeutical relationship or be positively and creatively integrated into session and inter-sessional dynamics. As technological advancements open scenarios where anyone could have access to a personal and all-knowing "oracle", the ability to formulate questions, individuals' experiences, and the scientific rigor with which clinicians study them must remain at the center of our work. Ethical and legal issues about the use of AI are also considered.

Short: Causal structural learning of conversational engagement for socially isolated older adults

Social isolation has become a growing public health concern in older adults and older adults with mild cognitive impairment. Coping strategies must be developed to increase social contact for socially isolated older adults. In this paper, we explored the conversational strategy between trained conversation moderators and socially isolated adults during a conversational engagement clinical trial (Clinicaltrials.gov: NCT02871921). We carried out structural learning and causality analysis to investigate the conversation strategies used by the trained moderators to engage socially isolated adults in the conversation and the causal effects of the strategy on engagement. Causal relations and effects were inferred between participants' emotions, the dialogue strategies used by moderators, and participants' following emotions. The results found in this paper may be used to support the development of cost-efficient, trustworthy AI- and/or robot-based platform to promote conversational engagement for older adults to address the challenges in social interaction.

Waiting for a digital therapist: three challenges on the path to psychotherapy delivered by artificial intelligence

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called "general" or "human-like" AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy.

Effects of a virtual voice-based coach delivering problem-solving treatment on emotional distress and brain function: a pilot RCT in depression and anxiety

Consumer-based voice assistants have the ability to deliver evidence-based treatment, but their therapeutic potential is largely unknown. In a pilot trial of a virtual voice-based coach, Lumen, delivering problem-solving treatment, adults with mild-to-moderate depression and/or anxiety were randomized to the Lumen intervention (n = 42) or waitlist control (n = 21). The main outcomes included changes in neural measures of emotional reactivity and cognitive control, and Hospital Anxiety and Depression Scale [HADS] symptom scores over 16 weeks. Participants were 37.8 years (SD = 12.4), 68% women, 25% Black, 24% Latino, and 11% Asian. Activation of the right dlPFC (neural region of interest in cognitive control) decreased in the intervention group but increased in the control group, with an effect size meeting the prespecified threshold for a meaningful effect (Cohen's d = 0.3). Between-group differences in the change in activation of the left dlPFC and bilateral amygdala were observed, but were of smaller magnitude (d = 0.2). Change in right dlPFC activation was also meaningfully associated (r ≥ 0.4) with changes in self-reported problem-solving ability and avoidance in the intervention. Lumen intervention also led to decreased HADS depression, anxiety, and overall psychological distress scores, with medium effect sizes (Cohen's d = 0.49, 0.51, and 0.55, respectively), compared with the waitlist control group. This pilot trial showed promising effects of a novel digital mental health intervention on cognitive control using neuroimaging and depression and anxiety symptoms, providing foundational evidence for a future confirmatory study.

Conversational Artificial Intelligence in Psychotherapy: A New Therapeutic Tool or Agent?

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape-such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses many risks that need in-depth ethical scrutiny. The objective of this paper is to complement current research on the ethics of AI for mental health by proposing a holistic, ethical, and epistemic analysis of CAI adoption. First, we focus on the question of whether CAI is rather a tool or an agent. This question serves as a framework for the subsequent ethical analysis of CAI focusing on topics of (self-) knowledge, (self-)understanding, and relationships. Second, we propose further conceptual and ethical analysis regarding human-AI interaction and argue that CAI cannot be considered as an equal partner in a conversation as is the case with a human therapist. Instead, CAI's role in a conversation should be restricted to specific functions.

A Multi-Industry Analysis of the Future Use of AI Chatbots

Artificial intelligence (AI) chatbots are set to be the defining technology of the next decade due to their ability to increase human capability at a low cost. However, more research is required to assess individuals’ behavioural intentions to use this technology when it becomes publicly available. This study applied an extended Technology Acceptance Model (TAM), with additional predictors of trust and privacy concerns, to assess individuals’ behavioural intentions to use AI chatbots across three industries: mental health care, online shopping, and online banking. These services were selected due to the current popularity of regular chatbots in these fields. Participants ( $N=360$ , 202 females) aged between 17 and 85 years ( $M=38.17$ , $\text{SD}=17.66$ ) completed a 71-item online, cross-sectional survey. As hypothesised, perceived usefulness and trust were significant positive predictors of behavioural intentions across all three behaviours. However, the influence of the perceived ease of use and privacy concerns on behavioural intentions differed across the three behaviours. These findings highlight that the combination of predictors within the extended TAM have different influences on behavioural intentions to use AI chatbots for mental health care, online shopping, and online banking. This research contributes to the literature by demonstrating that the influence of the variables in one field cannot be generalised across all uses of AI chatbots.

Computational Methods in Psychotherapy: A Scoping Review

BACKGROUND

The study of complex systems, such as the psychotherapeutic encounter, transcends the mechanistic and reductionist methods for describing linear processes and needs suitable approaches to describe probabilistic and scarcely predictable phenomena.

OBJECTIVE

The present study undertakes a scoping review of research on the computational methods in psychotherapy to gather new developments in this field and to better understand the phenomena occurring in psychotherapeutic interactions as well as in human interaction more generally.

DESIGN

Online databases were used to identify papers published 2011-2022, from which we selected 18 publications from different resources, selected according to criteria established in advance and described in the text. A flow chart and a summary table of the articles consulted have been created.

RESULTS

The majority of publications (44.4%) reported combined computational and experimental approaches, so we grouped the studies according to the types of computational methods used. All but one of the studies collected measured data. All the studies confirmed the usefulness of predictive and learning models in the study of complex variables such as those belonging to psychological, psychopathological and psychotherapeutic processes.

CONCLUSIONS

Research on computational methods will benefit from a careful selection of reference methods and standards. Therefore, this review represents an attempt to systematise the empirical literature on the applications of computational methods in psychotherapy research in order to offer clinicians an overview of the usefulness of these methods and the possibilities of their use in the various fields of application, highlighting their clinical implications, and ultimately attempting to identify potential opportunities for further research.

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review

Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient’s response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words “psychotherapy”, “artificial intelligence”, and “emotional disorders”. From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients’ needs, more methodologically robust studies are needed.

A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions

This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

Is Artificial Intelligence Better than Manpower? The Effects of Different Types of Online Customer Services on Customer Purchase Intentions

Artificial intelligence has been widely applied to e-commerce and the online business service field. However, few studies have focused on studying the differences in the effects of types of customer service on customer purchase intentions. Based on service encounter theory and superposition theory, we designed two shopping experiments to capture customers’ thoughts and feelings, in order to explore the differences in the effects of three different types of online customer service (AI customer service, manual customer service, and human–machine collaboration customer service) on customer purchase intention, and analyses the superposition effect of human–machine collaboration customer service. The results show that the consumer’s perceived service quality positively influences the customer’s purchase intention, and plays a mediating role in the effect of different types of online customer service on customer purchase intention; the product type plays a moderating role in the relationship between online customer service and customer purchase intention, and human–machine collaboration customer service has a superposition effect. This study helped to deepen the understanding of AI developers and e-commerce platforms regarding the application of AI in online business service, and provides reference suggestions for the formulation of more perfect business service strategies.

An overview of and recommendations for more accessible digital mental health services

Mental health concerns are common, and various evidence-based interventions for mental health conditions have been developed. However, many people have difficulty accessing appropriate mental health care and this has been exacerbated by the COVID-19 pandemic. Digital mental health services, such as those delivered by mobile phone or web-based platforms, offer the possibility of expanding the reach and accessibility of mental health care. To achieve this goal, digital mental health interventions and plans for their implementation must be designed with the end users in mind. In this Review, we describe the evidence base for digital mental health interventions across various diagnoses and treatment targets. Then, we explain the different formats for digital mental health intervention delivery, and offer considerations for their use across key age groups. We discuss the role that the COVID-19 pandemic has played in emphasizing the value of these interventions, and offer considerations for ensuring equity in access to digital mental health interventions among diverse populations. As healthcare providers continue to embrace the role that technology can play in broadening access to care, the design and implementation of digital mental healthcare solutions must be carefully considered to maximize their effectiveness and accessibility.

An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health

Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.

Perspectives fondamentale, clinique et sociétale de l’utilisation des personnages virtuels en santé mentale

Avec l’attrait engendré par les avancées en informatique et en intelligence artificielle, les personnages virtuels (c.-à-d. personnages représentés numériquement d’apparence humaine ou non) sont pressentis comme de futurs prestataires de soins en santé mentale. À ce jour, l’utilisation principale de tels personnages est toutefois marginale et se limite à une aide complémentaire à la pratique des cliniciens. Des préoccupations liées à la sécurité et l’efficacité, ainsi qu’un manque de connaissances et de compétences peuvent expliquer cette discordance entre ce que certains s’imaginent être l’utilisation future (voire futuriste) des personnages virtuels et leur utilisation actuelle. Un aperçu des récentes données probantes contribuerait à réduire cette divergence et à mieux saisir les enjeux associés à leur utilisation plus répandue en santé mentale.

Objectif Cet article vise à informer tous les acteurs impliqués, dont les cliniciens, quant au potentiel des personnages virtuels en santé mentale, et de les sensibiliser aux enjeux associés à leur usage.

Méthode Une recension narrative de la littérature a été réalisée afin de synthétiser les informations obtenues de la recherche fondamentale et clinique, et de discuter des considérations sociétales.

Résultats Plusieurs caractéristiques des personnages virtuels provenant de la recherche fondamentale ont le potentiel d’influencer les interactions entre un patient et un clinicien. Elles peuvent être regroupées en deux grandes catégories : les caractéristiques liées à la perception (p. ex. réalisme) et celles liées à l’attribution spontanée d’une catégorie sociale au personnage virtuel par un observateur (p. ex. genre). Selon la recherche clinique, plusieurs interventions ou évaluations utilisant des personnages virtuels ont montré divers degrés d’efficacité en santé mentale, et certains éléments de la relation thérapeutique (p. ex. alliance et empathie) peuvent d’ailleurs être présents lors d’une relation avec un personnage virtuel. De multiples enjeux socioéconomiques et éthiques doivent aussi être discutés en vue d’un développement et d’une utilisation plus accrue qui soient responsables et éthiques. Bien que l’accessibilité et la disponibilité des personnages virtuels constituent un avantage indéniable pour l’offre de services en santé mentale, certaines iniquités demeurent. L’accumulation de données biométriques (p. ex. rythme cardiaque) a également le potentiel d’enrichir le travail des cliniciens, mais aussi de mener au développement de personnages virtuels autonomes à l’aide de l’intelligence artificielle, ce qui pourrait conduire à certains dérapages (p. ex. erreurs de décision clinique). Quelques pistes de recommandations visant à éviter ces effets indésirables sont présentées.

Conclusion L’emploi des personnages virtuels sera de plus en plus répandu en santé mentale en raison de leurs avantages prometteurs. Ainsi, il est souhaitable que tous les acteurs impliqués s’informent sur leur usage dans ce contexte, se sensibilisent aux enjeux spécifiques, participent activement aux discussions quant à leur développement et adoptent des recommandations uniformes en vue d’un usage sécuritaire et éthique en santé mentale.

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.

The professional representation of conversational agents for health care: a scoping review protocol

OBJECTIVE

The purpose of this scoping review is to examine the professional representation of conversational agents that are used for health care. Professional characteristics associated with these agents will be identified, and the prevalence of these characteristics will be determined.

INTRODUCTION

Conversational agents that are used for health care lack the qualifications and capabilities of real health professionals, but this fact may not be clear to some patients and health seekers. This problem may be exacerbated when conversational agents are described as health professionals or are given professional titles or appearances. To date, the professional representation of conversational agents that are used for health care has received little attention in the literature.

INCLUSION CRITERIA

This review will include scholarly publications on conversational agents that are used for health care, particularly descriptive/developmental case studies and intervention/evaluation studies. This review will consider conversational agents designed for patients and health seekers, but not health professionals or trainees. Agents addressing physical and/or mental health will be considered.

METHODS

This review will be conducted in accordance with JBI methodology for scoping reviews. The databases to be searched will include MEDLINE (PubMed), Embase (Elsevier), CINAHL with Full Text (EBSCO), Scopus (Elsevier), Web of Science (Clarivate), ACM Guide to Computing Literature (ACM Digital Library), and IEEE Xplore (IEEE). The extracted data will include study characteristics, basic characteristics of the conversational agent, and characteristics relating to the professional representation of the conversational agent. The extracted data will be presented in tabular form and summarized using frequency analysis. These results will be accompanied by a narrative summary.

Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study

Background

Voice-controlled intelligent personal assistants (VIPAs), such as Amazon Echo and Google Home, involve artificial intelligence–powered algorithms designed to simulate humans. Their hands-free interface and growing capabilities have a wide range of applications in health care, covering off-clinic education, health monitoring, and communication. However, conflicting factors, such as patient safety and privacy concerns, make it difficult to foresee the further development of VIPAs in health care.

Objective

This study aimed to develop a plausible scenario for the further development of VIPAs in health care to support decision making regarding the procurement of VIPAs in health care organizations.

Methods

We conducted a two-stage Delphi study with an internationally recruited panel consisting of voice assistant experts, medical professionals, and representatives of academia, governmental health authorities, and nonprofit health associations having expertise with voice technology. Twenty projections were formulated and evaluated by the panelists. Descriptive statistics were used to derive the desired scenario.

Results

The panelists expect VIPAs to be able to provide solid medical advice based on patients’ personal health information and to have human-like conversations. However, in the short term, voice assistants might neither provide frustration-free user experience nor outperform or replace humans in health care. With a high level of consensus, the experts agreed with the potential of VIPAs to support elderly people and be widely used as anamnesis, informational, self-therapy, and communication tools by patients and health care professionals. Although users’ and governments’ privacy concerns are not expected to decrease in the near future, the panelists believe that strict regulations capable of preventing VIPAs from providing medical help services will not be imposed.

Conclusions

According to the surveyed experts, VIPAs will show notable technological development and gain more user trust in the near future, resulting in widespread application in health care. However, voice assistants are expected to solely support health care professionals in their daily operations and will not be able to outperform or replace medical staff.

Changes to the Psychiatric Chatbot Landscape: A Systematic Review of Conversational Agents in Serious Mental Illness: Changements du paysage psychiatrique des chatbots: une revue systématique des agents conversationnels dans la maladie mentale sérieuse

OBJECTIVE

The need for digital tools in mental health is clear, with insufficient access to mental health services. Conversational agents, also known as chatbots or voice assistants, are digital tools capable of holding natural language conversations. Since our last review in 2018, many new conversational agents and research have emerged, and we aimed to reassess the conversational agent landscape in this updated systematic review.

METHODS

A systematic literature search was conducted in January 2020 using the PubMed, Embase, PsychINFO, and Cochrane databases. Studies included were those that involved a conversational agent assessing serious mental illness: major depressive disorder, schizophrenia spectrum disorders, bipolar disorder, or anxiety disorder.

RESULTS

Of the 247 references identified from selected databases, 7 studies met inclusion criteria. Overall, there were generally positive experiences with conversational agents in regard to diagnostic quality, therapeutic efficacy, or acceptability. There continues to be, however, a lack of standard measures that allow ease of comparison of studies in this space. There were several populations that lacked representation such as the pediatric population and those with schizophrenia or bipolar disorder. While comparing 2018 to 2020 research offers useful insight into changes and growth, the high degree of heterogeneity between all studies in this space makes direct comparison challenging.

CONCLUSIONS

This review revealed few but generally positive outcomes regarding conversational agents' diagnostic quality, therapeutic efficacy, and acceptability, which may augment mental health care. Despite this increase in research activity, there continues to be a lack of standard measures for evaluating conversational agents as well as several neglected populations. We recommend that the standardization of conversational agent studies should include patient adherence and engagement, therapeutic efficacy, and clinician perspectives.

Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

Background

Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear.

Objective

This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs.

Methods

We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records.

Results

Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants’ previous experience with technology. Finally, risk bias varied markedly.

Conclusions

The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care.

A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study

Background

Misuse of substances is common, can be serious and costly to society, and often goes untreated due to barriers to accessing care. Woebot is a mental health digital solution informed by cognitive behavioral therapy and built upon an artificial intelligence–driven platform to deliver tailored content to users. In a previous 2-week randomized controlled trial, Woebot alleviated depressive symptoms.

Objective

This study aims to adapt Woebot for the treatment of substance use disorders (W-SUDs) and examine its feasibility, acceptability, and preliminary efficacy.

Methods

American adults (aged 18-65 years) who screened positive for substance misuse without major health contraindications were recruited from online sources and flyers and enrolled between March 27 and May 6, 2020. In a single-group pre/postdesign, all participants received W-SUDs for 8 weeks. W-SUDs provided mood, craving, and pain tracking and modules (psychoeducational lessons and psychotherapeutic tools) using elements of dialectical behavior therapy and motivational interviewing. Paired samples t tests and McNemar nonparametric tests were used to examine within-subject changes from pre- to posttreatment on measures of substance use, confidence, cravings, mood, and pain.

Results

The sample (N=101) had a mean age of 36.8 years (SD 10.0), and 75.2% (76/101) of the participants were female, 78.2% (79/101) were non-Hispanic White, and 72.3% (73/101) were employed. Participants’ W-SUDs use averaged 15.7 (SD 14.2) days, 12.1 (SD 8.3) modules, and 600.7 (SD 556.5) sent messages. About 94% (562/598) of all completed psychoeducational lessons were rated positively. From treatment start to end, in-app craving ratings were reduced by half (87/101, 86.1% reporting cravings in the app; odds ratio 0.48, 95% CI 0.32-0.73). Posttreatment assessment completion was 50.5% (51/101), with better retention among those who initially screened higher on substance misuse. From pre- to posttreatment, confidence to resist urges to use substances significantly increased (mean score change +16.9, SD 21.4; P<.001), whereas past month substance use occasions (mean change −9.3, SD 14.1; P<.001) and scores on the Alcohol Use Disorders Identification Test-Concise (mean change −1.3, SD 2.6; P<.001), 10-item Drug Abuse Screening Test (mean change −1.2, SD 2.0; P<.001), Patient Health Questionnaire-8 item (mean change 2.1, SD 5.2; P=.005), Generalized Anxiety Disorder-7 (mean change −2.3, SD 4.7; P=.001), and cravings scale (68.6% vs 47.1% moderate to extreme; P=.01) significantly decreased. Most participants would recommend W-SUDs to a friend (39/51, 76%) and reported receiving the service they desired (41/51, 80%). Fewer felt W-SUDs met most or all of their needs (22/51, 43%).

Conclusions

W-SUDs was feasible to deliver, engaging, and acceptable and was associated with significant improvements in substance use, confidence, cravings, depression, and anxiety. Study attrition was high. Future research will evaluate W-SUDs in a randomized controlled trial with a more diverse sample and with the use of greater study retention strategies.

Trial Registration

ClinicalTrials.gov NCT04096001; http://clinicaltrials.gov/ct2/show/NCT04096001.

A scoping review of machine learning in psychotherapy research

Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).

Assessing the accuracy of automatic speech recognition for psychotherapy

Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80% and positive predictive value was 83%. For clinician-identified harm-related sentences, the word error rate was 34%. These results suggest that automatic speech recognition may support understanding of language patterns and subgroup variation in existing treatments but may not be ready for individual-level safety surveillance.