Feasibility and impact of a mental health chatbot on postpartum mental health: a randomized controlled trial

Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis

BACKGROUND

The introduction of natural language processing (NLP) technologies has significantly enhanced the potential of self-administered interventions for treating anxiety and depression by improving human-computer interactions. Although these advances, particularly in complex models such as generative artificial intelligence (AI), are highly promising, robust evidence validating the effectiveness of the interventions remains sparse.

OBJECTIVE

The aim of this study was to determine whether self-administered interventions based on NLP models can reduce depressive and anxiety symptoms.

METHODS

We conducted a systematic review and meta-analysis. We searched Web of Science, Scopus, MEDLINE, PsycINFO, IEEE Xplore, Embase, and Cochrane Library from inception to November 3, 2023. We included studies with participants of any age diagnosed with depression or anxiety through professional consultation or validated psychometric instruments. Interventions had to be self-administered and based on NLP models, with passive or active comparators. Outcomes measured included depressive and anxiety symptom scores. We included randomized controlled trials and quasi-experimental studies but excluded narrative, systematic, and scoping reviews. Data extraction was performed independently by pairs of authors using a predefined form. Meta-analysis was conducted using standardized mean differences (SMDs) and random effects models to account for heterogeneity.

RESULTS

In all, 21 articles were selected for review, of which 76% (16/21) were included in the meta-analysis for each outcome. Most of the studies (16/21, 76%) were recent (2020-2023), with interventions being mostly AI-based NLP models (11/21, 52%); most (19/21, 90%) delivered some form of therapy (primarily cognitive behavioral therapy: 16/19, 84%). The overall meta-analysis showed that self-administered interventions based on NLP models were significantly more effective in reducing both depressive (SMD 0.819, 95% CI 0.389-1.250; P<.001) and anxiety (SMD 0.272, 95% CI 0.116-0.428; P=.001) symptoms compared to various control conditions. Subgroup analysis indicated that AI-based NLP models were effective in reducing depressive symptoms (SMD 0.821, 95% CI 0.207-1.436; P<.001) compared to pooled control conditions. Rule-based NLP models showed effectiveness in reducing both depressive (SMD 0.854, 95% CI 0.172-1.537; P=.01) and anxiety (SMD 0.347, 95% CI 0.116-0.578; P=.003) symptoms. The meta-regression showed no significant association between participants' mean age and treatment outcomes (all P>.05). Although the findings were positive, the overall certainty of evidence was very low, mainly due to a high risk of bias, heterogeneity, and potential publication bias.

CONCLUSIONS

Our findings support the effectiveness of self-administered NLP-based interventions in alleviating depressive and anxiety symptoms, highlighting their potential to increase accessibility to, and reduce costs in, mental health care. Although the results were encouraging, the certainty of evidence was low, underscoring the need for further high-quality randomized controlled trials and studies examining implementation and usability. These interventions could become valuable components of public health strategies to address mental health issues.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42023472120; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023472120.

Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review

BACKGROUND

Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain.

OBJECTIVE

This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature.

METHODS

Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques).

RESULTS

In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.

CONCLUSIONS

This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

BACKGROUND

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

OBJECTIVE

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

METHODS

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

RESULTS

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

CONCLUSIONS

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

The therapeutic effectiveness of artificial intelligence-based chatbots in alleviation of depressive and anxiety symptoms in short-course treatments: A systematic review and meta-analysis

BACKGROUND

The emergence of artificial intelligence-based chatbot has revolutionized the field of clinical psychology and psychotherapy, granting individuals unprecedented access to professional assistance, overcoming time constraints and geographical limitations with cost-effective convenience. However, despite its potential, there has been a noticeable gap in the literature regarding their effectiveness in addressing common mental health issues like depression and anxiety. This meta-analysis aims to evaluate the efficacy of AI-based chatbots in treating these conditions.

METHODS

A systematic search was executed across multiple databases, including PubMed, Cochrane Library, Web of Science, PsycINFO, and Embase on April 4th, 2024. The effect size of treatment efficacy was calculated using the standardized mean difference (Hedge's g). Quality assessment measures were implemented to ensure trial's quality.

RESULTS

In our analysis of 18 randomized controlled trials involving 3477 participants, we observed noteworthy improvements in depression (g = -0.26, 95 % CI = -0.34, -0.17) and anxiety (g = -0.19, 95 % CI = -0.29, -0.09) symptoms. The most significant benefits were evident after 8 weeks of treatment. However, at the three-month follow-up, no substantial effects were detected for either condition.

LIMITATIONS

Several limitations should be considered. These include the lack of diversity in the study populations, variations in chatbot design, and the use of different psychotherapeutic approaches. These factors may limit the generalizability of our findings.

CONCLUSION

This meta-analysis highlights the promising role of AI-based chatbot interventions in alleviating depressive and anxiety symptoms among adults. Our results indicate that these interventions can yield substantial improvements over a relatively brief treatment period.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

Could artificial intelligence write mental health nursing care plans?

WHAT IS KNOWN ON THE SUBJECT?: Artificial intelligence (AI) is freely available, responds to very basic text input (such as a question) and can now create a wide range of outputs, communicating in many languages or art forms. AI platforms like OpenAI's ChatGPT can now create passages of text that could be used to create plans of care for people with mental health needs. As such, AI output can be difficult to distinguish from human-output, and there is a risk that its use could go unnoticed. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Whilst it is known that AI can produce text or pass pre-registration health-profession exams, it is not known if AI can produce meaningful results for care delivery. We asked ChatGPT basic questions about a fictitious person who presents with self-harm and then evaluated the quality of the output. We found that the output could look reasonable to laypersons but there were significant errors and ethical issues. There are potential harms to people in care if AI is used without an expert correcting or removing these errors. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: We suggest that there is a risk that AI use could cause harm if it was used in direct care delivery. There is a lack of policy and research to safeguard people receiving care - and this needs to be in place before AI should be used in this way. Key aspects of the role of a mental health nurse are relational and AI use may diminish mental health nurses' ability to provide safe care in its current form. Many aspects of mental health recovery are linked to relationships and social engagement, however AI is not able to provide this and may push the people who are in most need of help further away from services that assist recovery. ABSTRACT: Background Artificial intelligence (AI) is being increasingly used and discussed in care contexts. ChatGPT has gained significant attention in popular and scientific literature although how ChatGPT can be used in care-delivery is not yet known. Aims To use artificial intelligence (ChatGPT) to create a mental health nursing care plan and evaluate the quality of the output against the authors' clinical experience and existing guidance. Materials & Methods Basic text commands were input into ChatGPT about a fictitious person called 'Emily' who presents with self-injurious behaviour. The output from ChatGPT was then evaluated against the authors' clinical experience and current (national) care guidance. Results ChatGPT was able to provide a care plan that incorporated some principles of dialectical behaviour therapy, but the output had significant errors and limitations and thus there is a reasonable likelihood of harm if used in this way. Discussion AI use is increasing in direct-care contexts through the use of chatbots or other means. However, AI can inhibit clinician to care-recipient engagement, 'recycle' existing stigma, and introduce error, which may thus diminish the ability for care to uphold personhood and therefore lead to significant avoidable harms. Conclusion Use of AI in this context should be avoided until a point where policy and guidance can safeguard the wellbeing of care recipients and the sophistication of AI output has increased. Given ChatGPT's ability to provide superficially reasonable outputs there is a risk that errors may go unnoticed and thus increase the likelihood of patient harms. Further research evaluating AI output is needed to consider how AI may be used safely in care delivery.

Rosie, a Health Education Question-and-Answer Chatbot for New Mothers: Randomized Pilot Study

BACKGROUND

Stark disparities exist in maternal and child outcomes and there is a need to provide timely and accurate health information.

OBJECTIVE

In this pilot study, we assessed the feasibility and acceptability of a health chatbot for new mothers of color.

METHODS

Rosie, a question-and-answer chatbot, was developed as a mobile app and is available to answer questions about pregnancy, parenting, and child development. From January 9, 2023, to February 9, 2023, participants were recruited using social media posts and through engagement with community organizations. Inclusion criteria included being aged ≥14 years, being a woman of color, and either being currently pregnant or having given birth within the past 6 months. Participants were randomly assigned to the Rosie treatment group (15/29, 52% received the Rosie app) or control group (14/29, 48% received a children's book each month) for 3 months. Those assigned to the treatment group could ask Rosie questions and receive an immediate response generated from Rosie's knowledgebase. Upon detection of a possible health emergency, Rosie sends emergency resources and relevant hotline information. In addition, a study staff member, who is a clinical social worker, reaches out to the participant within 24 hours to follow up. Preintervention and postintervention tests were completed to qualitatively and quantitatively evaluate Rosie and describe changes across key health outcomes, including postpartum depression and the frequency of emergency room visits. These measurements were used to inform the clinical trial's sample size calculations.

RESULTS

Of 41 individuals who were screened and eligible, 31 (76%) enrolled and 29 (71%) were retained in the study. More than 87% (13/15) of Rosie treatment group members reported using Rosie daily (5/15, 33%) or weekly (8/15, 53%) across the 3-month study period. Most users reported that Rosie was easy to use (14/15, 93%) and provided responses quickly (13/15, 87%). The remaining issues identified included crashing of the app (8/15, 53%), and users were not satisfied with some of Rosie's answers (12/15, 80%). Mothers in both the Rosie treatment group and control group experienced a decline in depression scores from pretest to posttest periods, but the decline was statistically significant only among treatment group mothers (P=.008). In addition, a low proportion of treatment group infants had emergency room visits (1/11, 9%) compared with control group members (3/13, 23%). Nonetheless, no between-group differences reached statistical significance at P<.05.

CONCLUSIONS

Rosie was found to be an acceptable, feasible, and appropriate intervention for ethnic and racial minority pregnant women and mothers of infants owing to the chatbot's ability to provide a personalized, flexible tool to increase the timeliness and accessibility of high-quality health information to individuals during a period of elevated health risks for the mother and child.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06053515; https://clinicaltrials.gov/study/NCT06053515.

Practical application of digital therapeutics in people with mood disorders

Digital therapeutics (DTx) offer evidence-based digitally-delivered high quality standards applications and/or softwares in the prevention, management and treatment of several medical conditions, including mood disorders. Nowadays, there are only three DTx officially approved by the Food and Drug Administration for mental conditions and there are still very few DTx developed in the context of mood disorders. The current comprehensive overview aims at providing a summary of currently published studies on DTx clinical applications in major depressive disorder (MDD), depressive symptomatology and bipolar disorder (BD), by using PubMed/MEDLINE and Scopus databases. Fifteen studies have been selected (10 on DTx in depressive symptomatology and/or MDD; 4 on BD; 1 on MDD and BD). Literature on DTx in mood disorders is still lacking, being mostly constituted by feasibility and acceptability rather than efficacy/effectiveness outcomes, particularly in BD. More studies focused on MDD compared to BD. Most DTx on MDD have been developed based on cognitive behaviour therapy interventions while on BD are based on psychoeducation. All studies assessing symptom severity improvement pre- vs. postinterventions demonstrated a significant postintervention improvement. Therefore, despite the preliminary encouraging results of studies here retrieved, their methodology is still too heterogeneous to allow comparisons and the generalizability of their findings. Further studies are warranted, in more larger samples involving multiple sites, including measures of both specific symptom effects as well as acceptability, feasibility and effectiveness in the real-world settings.

Anatomy of a Woebot® (WB001): agent guided CBT for women with postpartum depression

INTRODUCTION

Postpartum depression (PPD) is common, persistent, and stigmatized. There are insufficient trained professionals to deliver appropriate screening, diagnosis, and treatment.

AREAS COVERED

WB001 is a Software as a Medical Device (SaMD) based Agent-Guided Cognitive-Behavioral Therapy (AGCBT) program for the treatment of PPD, for which Breakthrough Device Designation was recently granted by the US Food and Drug Administration. WB001 combines therapeutic alliance, human-centered design, machine learning techniques, and established principles from CBT and interpersonal therapy (IPT). We introduce AGCBT as a new model of service delivery, whilst describing Woebot, the agent technology that enables guidance through the replication of some elements of human relationships. The profile describes the device's design principles, enabling technology, risk handling, and efficacy data in PPD.

EXPERT OPINION

WB001 is a dynamic and personalized tool with which patients may establish a therapeutic bond. Woebot is designed to augment (rather than replace) human healthcare providers, unlocking the therapeutic potency associated with guidance, whilst retaining the scalability and agency that characterizes self-help approaches. WB001 has the potential to improve both the quality and the scalability of care through providing support to patients on waiting lists, in between clinical encounters, and enabling automation of measurement-based-care.

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.