Artificial Intelligence in Mental Health

2022 ◽  
pp. 296-319
Author(s):  
Lisa Ogilvie ◽  
Julie Prescott ◽  
Terry Hanley ◽  
Jerome Carson
Keyword(s):  
Mental Health ◽  
Language Processing ◽  
Human Interaction ◽  
Ethical Framework ◽  
Conversational Agents ◽  
Addiction Recovery ◽  
Resultant Solution ◽  
Common Risk Factors ◽  
The Common ◽  
Technological Improvements

Chatbots are programmed conversational agents that emulate communication systematically using natural language processing. They can be programmed to assume a range of roles where regular human interaction occurs. Within mental health services, they are not as well represented as in other areas of healthcare, with research suggesting that uptake has been hindered by concerns over the accuracy of the information they provide, undeveloped technology, lack of adherence to an ethical framework, and the unconvincing portrayal of human authenticity. Technological improvements have addressed some of these concerns, and as the resultant solution choice increases, the potential for chatbots within mental health is receiving greater attention. In this chapter, two novel uses for chatbots are showcased. Foxbot, a recovery friend, accessible at the point of need to help mitigate some of the common risk factors to sustaining addiction recovery; and ERIC, a counselling client who allows trainee counsellors to practise their counselling skills without having to enlist an actual client.

scholarly journals Is the Automation of Digital Mental Health Ethical? Applying an Ethical Framework to Chatbots for Cognitive Behaviour Therapy

2021 ◽  
Vol 3 ◽  
Author(s):  
Giovanna Nunes Vilaza ◽  
Darragh McCashin
Keyword(s):  
Mental Health ◽  
Research Policy ◽  
Behavioural Therapy ◽  
Ethical Framework ◽  
Conversational Agents ◽  
Behaviour Therapy ◽  
Face To Face ◽  
Cognitive Behavioural ◽  
Cognitive Behaviour ◽  
Mental Health Support

The COVID-19 pandemic has intensified the need for mental health support across the whole spectrum of the population. Where global demand outweighs the supply of mental health services, established interventions such as cognitive behavioural therapy (CBT) have been adapted from traditional face-to-face interaction to technology-assisted formats. One such notable development is the emergence of Artificially Intelligent (AI) conversational agents for psychotherapy. Pre-pandemic, these adaptations had demonstrated some positive results; but they also generated debate due to a number of ethical and societal challenges. This article commences with a critical overview of both positive and negative aspects concerning the role of AI-CBT in its present form. Thereafter, an ethical framework is applied with reference to the themes of (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. These themes are then discussed in terms of practical recommendations for future developments. Although automated versions of therapeutic support may be of appeal during times of global crises, ethical thinking should be at the core of AI-CBT design, in addition to guiding research, policy, and real-world implementation as the world considers post-COVID-19 society.

scholarly journals A Context-Aware Conversational Agent in the Rehabilitation Domain

2019 ◽  
Vol 11 (11) ◽  
pp. 231 ◽  
Author(s):  
Thanassis Mavropoulos ◽  
Georgios Meditskos ◽  
Spyridon Symeonidis ◽  
Eleni Kamateri ◽  
Maria Rousi ◽  
...  
Keyword(s):  
Language Processing ◽  
Speech Synthesis ◽  
Theoretical Background ◽  
Human Interaction ◽  
Context Aware ◽  
Sensor Management ◽  
Vision Sensor ◽  
Conversational Agents ◽  
Research Areas ◽  
Intuitive Manner

Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.

A fully automated conversational agent with artificial intelligence capabilities (Youper) for treating depression and anxiety: A nonrandomized feasibility trial (Preprint)

2019 ◽  
Author(s):  
Jose Hamilton Vargas ◽  
Thiago Antonio Marafon ◽  
Diego Fernando Couto ◽  
Ricardo Giglio ◽  
Marvin Yan ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Financial Incentives ◽  
Empirical Support ◽  
Digital Technologies ◽  
Feasibility Trial ◽  
Depression And Anxiety ◽  
Conversational Agents ◽  
Symptoms Of Depression ◽  
Viable Solution

BACKGROUND Mental health conditions, including depression and anxiety disorders, are significant global concerns. Many people with these conditions don't get the help they need because of the high costs of medical treatment and the stigma attached to seeking help. Digital technologies represent a viable solution to these challenges. However, these technologies are often characterized by relatively low adherence and their effectiveness largely remains empirical unverified. While digital technologies may represent a viable solution for this persisting problem, they often lack empirical support for their effectiveness and are characterized by relatively low adherence. Conversational agents using artificial intelligence capabilities have the potential to offer a cost-effective, low-stigma and engaging way of getting mental health care. OBJECTIVE The objective of this study was to evaluate the feasibility, acceptability, and effectiveness of Youper, a mobile application that utilizes a conversational interface and artificial intelligence capabilities to deliver cognitive behavioral therapy-based interventions to reduce symptoms of depression and anxiety in adults. METHODS 1,012 adults with symptoms of depression and anxiety participated in a real-world setting study, entirely remotely, unguided and with no financial incentives, over an 8-week period. Participants completed digital versions of the 9-item Patient Health Questionnaire (PHQ-9) and the 7-item Generalized Anxiety Disorder scale (GAD-7) at baseline, 2, 4, and 8 weeks. RESULTS After the eight-week study period, depression (PHQ-9) scores of participants decreased by 48% while anxiety (GAD-7) scores decreased by 43%. The RCI was outside 2 standard deviations for 93.0% of the individuals in the PHQ-9 assessment and 90.7% in the GAD-7 assessment. Participants were on average 24.79 years old (SD 7.61) and 77% female. On average, participants interacted with Youper 0.9 (SD 1.56) times per week. CONCLUSIONS Results suggest that Youper is a feasible, acceptable, and effective intervention for adults with depression and anxiety. CLINICALTRIAL Since this study involved a nonclinical population, it wasn't registered in a public trials registry.

App review of anxiety and depression chatbots and their self-care features (Preprint)

2020 ◽  
Author(s):  
Arfan Ahmed ◽  
Nashva ALi ◽  
Sarah Aziz ◽  
Alaa A Abd-Alrazaq ◽  
Asmaa Hassan ◽  
...  
Keyword(s):  
Mental Health ◽  
Meta Analysis ◽  
Self Care ◽  
Anxiety And Depression ◽  
Conversational Agents ◽  
Eligibility Criteria ◽  
On Demand ◽  
User Ratings ◽  
Google Play

BACKGROUND Anxiety and depression rates are at an all-time high along with other mental health disorders. Smartphone-based mental health chatbots or conversational agents can aid psychiatrists and replace some of the costly human based interaction and represent a unique opportunity to expand the availability and quality of mental health services and treatment. Regular up-to-date reviews will allow medics and individuals to recommend or use anxiety and depression related smartphone based chatbots with greater confidence. OBJECTIVE Assess the quality and characteristics of chatbots for anxiety and depression available on Android and iOS systems. METHODS A search was performed in the App Store and Google Play Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing chatbots for anxiety and depression. Eligibility of the chatbots was assessed by two individuals based on predefined eligibility criteria. Meta-data of the included chatbots and their characteristics were extracted from their description and upon installation by 2 reviewers. Finally, chatbots quality information was assessed by following the mHONcode principles. RESULTS Although around 1000 anxiety and depression related chatbots exist, only a few (n=11) contained actual chatbots that could provide the user a real substitute for a human-human based interaction, even with today's Artificial Intelligence advancements, only one of these chatbots had voice as an input/output modality. Of the selected apps that contained chatbots all were clearly built with a therapeutic human substitute goal in mind. The majority had high user ratings and downloads highlighting the popularity of such chatbots and their promising future within the realm of anxiety and depression. CONCLUSIONS Anxiety and depression chatbot apps have the potential to increase the capacity of mental health self-care providing much needed assistance to professionals. In the current covid-19 pandemic, chatbots can also serve as a conversational companion with the potential of combating loneliness, especially in lockdowns where there is a lack of social interaction. Due to the ubiquitous nature of chatbots users can access them on-demand at the touch of a screen on ones’ smartphone. Self-care interventions are known to be effective and exist in various forms and some can be made available as chatbot features, such as assessment, mood tracking, medicine tracking, or simply providing conversation in times of loneliness.

Progressive Catholicism in the United States

2018 ◽  
Author(s):  
Matthew A. Shadle
Keyword(s):  
Common Good ◽  
The United States ◽  
Economic Life ◽  
Ethical Framework ◽  
Social Teaching ◽  
American Catholicism ◽  
The Common Good ◽  
The Us ◽  
The Common ◽  
American Society

American Catholicism has long adapted to US liberal institutions. Progressive Catholicism has taken the liberal values of democratic participation and human rights and made them central to its interpretation of Catholic social teaching. This chapter explores in detail the thought of David Hollenbach, S.J., a leading representative of progressive Catholicism. Hollenbach has proposed an ethical framework for an economy aimed at the common good, ensuring that the basic needs of all are met and that all are able to participate in economic life. The chapter also looks at the US Catholic bishops’ 1986 pastoral letter Economic Justice for All, which emphasizes similar themes while also promoting collaboration between the different sectors of American society for the sake of the common good.

scholarly journals The common risk factors for progression and mortality in COVID-19 patients: a meta-analysis

2021 ◽  
Author(s):  
Li Zhang ◽  
Jie Hou ◽  
Fu-Zhe Ma ◽  
Jia Li ◽  
Shuai Xue ◽  
...  
Keyword(s):  
Risk Factors ◽  
Meta Analysis ◽  
Common Risk Factors ◽  
The Common

A Systematic Review on Airbag-Induced Burns

2020 ◽  
Author(s):  
Sarah P Erpenbeck ◽  
Eva Roy ◽  
Jenny A Ziembicki ◽  
Francesco M Egro
Keyword(s):  
Systematic Review ◽  
Case Reports ◽  
Good Prognosis ◽  
Chemical Burns ◽  
Automobile Crashes ◽  
Chemical Mechanisms ◽  
Number Of Patients ◽  
Common Risk Factors ◽  
The Common ◽  
Ocular Burns

Abstract Airbags significantly reduce fatalities and injuries in automobile crashes, but they have been found to be associated with burns. Specifically, airbags can cause burns through thermal or chemical mechanisms and commonly affect the arms, hands, face, and eyes. While most airbag-induced burns are minor, some may cause unfavorable outcomes. Our study aimed to systematically review airbag-induced burns to assess etiology, type, and treatment of these injuries. A systematic review of case reports pertaining to airbag-induced cutaneous and ocular burns was conducted. Data reviewed included type/location of burns, severity of burn, total number of patients, treatment, complications, and outcome after treatment. We identified 21 case reports that met our inclusion criteria with a total of 24 patients reported in the studies. Of the studies identified, 38% were chemical burns and 25% were thermal burns. Most commonly the upper extremities were burned in 42% of cases, followed by eyes (25%) and face (21%). Most burns identified were superficial partial thickness (58%). Treatment outcomes were good for cutaneous burns, with 95% healing without complication. However, ocular injuries lead to permanent impaired eye function in 71% of cases. In our systematic review, we highlighted the common risk factors, prognosis, and treatment for thermal, chemical, and ocular burns. Airbag-induced burns have a relatively good prognosis but must be recognized and treated immediately to reduce the risk of serious sequelae.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

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