An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

BACKGROUND

Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation.

OBJECTIVE

This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised.

METHODS

We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis.

RESULTS

We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance.

CONCLUSIONS

Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Background

Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways.

Objective

This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work.

Methods

We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module.

Results

The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations.

Conclusions

Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

Internet Cognitive Behavioral Therapy With or Without Face-to-Face Psychotherapy: A 12-Weeks Clinical Trial of Patients With Depression

Depressive disorders are a curable, global health problem. However, most patients remain untreated, and more and more patients use internet-based interventions, but it is unclear whether it is beneficial for ongoing face-to-face psychotherapy. Thus, we compared the outcome of internet cognitive behavioral therapy (ICBT) with (ICBT+) or without (ICBT) additional face-to-face outpatient psychotherapy in adult patients with moderate to severe depressive disorder. For this longitudinal interventional clinical trial (NCT02112266), 168 of 252 online recruited adults with depressive symptoms received ICBT+ (n = 96) or ICBT (n = 72). Demographics (sex, age, age at first depressive episode, years of education, duration of depressive symptoms) were assessed and compared between groups. All patients underwent ICBT for 12 weeks. Quality of life (QoL) and severity of depressive symptoms were assessed within each group at three time points [baseline (T0), postinterventional after ICBT at 12 weeks (T1), and for follow-up at 6 months (T2)] using the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) global score to assess QoL as primary and the Beck Depression Inventory (BDI-II) to assess self-rated depressive symptoms as secondary outcome variables, respectively. Differences were assessed between groups using t test and over time using repeated-measures analysis of variance. Data of intention-to-treat analysis are given as mean ± SD. Group differences were assumed at p < 0.05. Partial η² is given as effect size. Demographic data, QoL, and depressive symptoms did not differ between groups (ICBT+/ICBT) at baseline (T0). Patients of both groups suffered from moderate to severe depressive disorders and gained improved QoL scores (WHOQOL-BREF-global: p < 0.001, η² = 0.16), as well as experienced decreased depressive symptoms (BDI-II: p < 0.001, η² = 0.2) after 12 weeks of ICBT compared to baseline. Patients without additional face-to-face outpatient psychotherapy lost QoL-albeit not significant-and had increased depressive symptoms (BDI: p = 0.02, η² = 0.04) at 6 months' follow-up. Thus, ICBT is suitable for psychiatric treatment, although additional face-to-face outpatient psychotherapy helps stabilizing long-term outcome.

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review (Preprint).. [DOI: 10.2196/preprints.19127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways.

OBJECTIVE

This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work.

METHODS

We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module.

RESULTS

The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations.

CONCLUSIONS

Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.