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Citation: Balcombe, L. AI Chatbots in
Digital Mental Health. Informatics
2023,10, 82. https://doi.org/
10.3390/informatics10040082
Academic Editors: Antony Bryant,
Roberto Montemanni, Min Chen,
Paolo Bellavista, Kenji Suzuki,
Jeanine Treffers-Daller and
Roberto Theron
Received: 3 September 2023
Revised: 17 October 2023
Accepted: 25 October 2023
Published: 27 October 2023
Copyright: © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
informatics
Review
AI Chatbots in Digital Mental Health
Luke Balcombe
Australian Institute for Suicide Research and Prevention, School of Applied Psychology, Griffith University,
Messines Ridge Road, Mount Gravatt, QLD 4122, Australia; lukebalcombe@gmail.com
Abstract:
Artificial intelligence (AI) chatbots have gained prominence since 2022. Powered by big
data, natural language processing (NLP) and machine learning (ML) algorithms, they offer the
potential to expand capabilities, improve productivity and provide guidance and support in various
domains. Human–Artificial Intelligence (HAI) is proposed to help with the integration of human
values, empathy and ethical considerations into AI in order to address the limitations of AI chatbots
and enhance their effectiveness. Mental health is a critical global concern, with a substantial impact
on individuals, communities and economies. Digital mental health solutions, leveraging AI and ML,
have emerged to address the challenges of access, stigma and cost in mental health care. Despite
their potential, ethical and legal implications surrounding these technologies remain uncertain. This
narrative literature review explores the potential of AI chatbots to revolutionize digital mental health
while emphasizing the need for ethical, responsible and trustworthy AI algorithms. The review is
guided by three key research questions: the impact of AI chatbots on technology integration, the
balance between benefits and harms, and the mitigation of bias and prejudice in AI applications.
Methodologically, the review involves extensive database and search engine searches, utilizing
keywords related to AI chatbots and digital mental health. Peer-reviewed journal articles and media
sources were purposively selected to address the research questions, resulting in a comprehensive
analysis of the current state of knowledge on this evolving topic. In conclusion, AI chatbots hold
promise in transforming digital mental health but must navigate complex ethical and practical
challenges. The integration of HAI principles, responsible regulation and scoping reviews are crucial
to maximizing their benefits while minimizing potential risks. Collaborative approaches and modern
educational solutions may enhance responsible use and mitigate biases in AI applications, ensuring a
more inclusive and effective digital mental health landscape.
Keywords: human–artificial intelligence; AI chatbots; digital mental health; mental health care
1. Introduction
Artificial intelligence (AI) chatbots are intelligent conversational computer systems
that think, learn and complete tasks in combination with humans or independently, us-
ing big data, natural language processing (NLP) and machine learning (ML) algorithms
to expand their capabilities, improve productivity and provide conversation, guidance
and support [
1
,
2
]. Also indicated as conversational agents or generative AI using large
language models, they are a result of progress in the past 15 years in the fields of robotics,
ML, AI models and NLP. AI chatbots became eminent since the launch of ChatGPT in
November 2022 [3].
There are opportunities where AI chatbots can provide insightful responses beyond
human capacity. However, they may lack a personalized and empathetic touch. It is
proposed that human–artificial intelligence (HAI) may help overcome such limitations,
whereby humans and AI enable each other’s strengths to collaborate on a common task or
goal for efficient, safer, sustainable and enjoyable work and lives. The HAI concept aligns
with the Center of Humane Technology’s work with integrating human values, such as
empathy, compassion and responsibility in AI [4].
Informatics 2023,10, 82. https://doi.org/10.3390/informatics10040082 https://www.mdpi.com/journal/informatics
Informatics 2023,10, 82 2 of 16
Mental health is a critical issue that affects many millions of people worldwide [
5
].
As an example, around 20% of Australian adults have a mental disorder, which increases
to 44% when the experience of mental illness is considered over a lifetime [
6
], costing the
economy billions of dollars [
7
] because of diminished health and reduced life expectancy [
8
].
Unfortunately, many people do not receive the help they need due to various barriers such
as a lack of access to mental health services, stigma and cost [
9
–
12
]. Digital mental health
solutions target young people with technology for mental health assessment, support,
prevention and treatment [
13
]. For example, AI and ML models are used in the prediction
of mental illness [
14
], and AI chatbots are used for psychological support [
15
]. However,
there is uncertainty around the ethical and legal implications of these tools.
The aim of this narrative literature review is to demonstrate how the potential for
AI chatbots to assist various populations with accessible digital mental health through
relevant, scalable and sustainable data-driven insights is challenged by the need for creating
ethical, responsible and trustworthy AI algorithms.
2. Methods
This narrative literature review is adapted from the four steps outlined by
Demiris et al. [16]:
(1) Conduct a search of numerous databases and search engines; (2) Identify and use perti-
nent keywords from relevant articles; (3) Review the abstracts and text of relevant articles
and include those that address the research aim; and (4) Document results by summarizing
and synthesizing the findings and integrating them into the review.
The heterogeneity of the topic prevented a systematic review. In addition, the topic
of AI chatbots in digital mental health is still evolving, and there were not enough studies
that meet the strict criteria of a systematic review. Additionally, the field of digital mental
health is interdisciplinary, incorporating aspects of psychology, technology and health
care. This leads to a wide range of research approaches, methodologies and study designs,
making it challenging to apply strict systematic review criteria. Instead, a purposively
selected articles approach showed an educational approach to how AI chatbots may impact
digital mental health. By purposively selecting articles that aligned with the research aim
and reviewing them in a comprehensive manner, valuable insights were gathered and are
presented in a coherent narrative literature review. This approach allowed for flexibility in
considering various perspectives and ideas within the topic, contributing to a more holistic
understanding of the subject matter.
The selection of peer-reviewed journal articles, media articles and conference proceed-
ings were retrieved from searches of computerized databases, purposive online searches
and authoritative texts based on an assessment of three of the research questions posed in
the Editorial, “AI Chatbots: Threat or Opportunity?” [
3
]. These research questions were
used as a guide to explore the topic of interest, because it is not yet possible to arrive at a
comprehensive understanding of the state of the science:
1.
The development of AI chatbots has been claimed to herald a new era, offering signif-
icant advances in the incorporation of technology into people’s lives and interactions.
Is this likely to be the case, and if so, where will these impacts be the most pervasive
and effective?
2.
Is it possible to strike a balance regarding the impact of these technologies so that any
potential harms are minimized while potential benefits are maximized and shared?
3.
A growing body of evidence shows that the design and implementation of many
AI applications, i.e., algorithms, incorporate bias and prejudice. How can this be
countered and corrected?
The database searches used were Scopus, ScienceDirect, Sage and the Association for
Computing Machinery (ACM) Digital Library. The search engines used were PubMed,
Google Scholar and IEEE Xplore. The search terms used were “AI chatbots” OR “generative
artificial intelligence” OR “conversational agents” AND “digital mental health” OR “mental
health care”.
The following selection of criteria were used:
Informatics 2023,10, 82 3 of 16
Inclusion criteria:
•
Studies that have been published in peer-reviewed journals, media articles and
conference proceedings.
•Studies that have been published in the English language.
•Studies that have been published between 2010 and 2023.
•
Studies that have investigated the use of AI chatbots, generative artificial intelligence
or conversational agents in digital mental health or mental health care.
•
Studies that have reported on the effectiveness of AI chatbots, generative artificial
intelligence or conversational agents in digital mental health or mental health care.
Exclusion criteria:
•
Studies that are not published in peer-reviewed journals, media articles and
conference proceedings.
•Studies that are not published in the English language.
•Studies that are published before 2010 or after 2023.
•
Studies that do not investigate the use of AI chatbots, generative artificial intelligence
or conversational agents in digital mental health or mental health care.
•
Studies that do not report on the effectiveness of AI chatbots, generative artificial
intelligence or conversational agents in digital mental health or mental health care.
Boolean operators such as AND and OR were used to combine search terms and refine
search results. For example, using the Boolean operator OR between “AI chatbots” and
“generative artificial intelligence” retrieved articles that contain either one of these terms.
Similarly, using the Boolean operator AND between “conversational agents” and “digital
mental health” retrieved articles that contain both these terms. Boolean operators helped to
narrow down search results and make them more relevant to the research question.
Relevant articles and their reference lists were explored based on (1) relevance to the
guiding research questions, (2) showing examples of theoretical and empirical research
and development and (3) highlighting issues and possible solutions. These articles were
applied in a best-evidence synthesis for a complete, critical and objective analysis of the
current knowledge on the topic. Overall, the method shows a systematic and transparent
approach that minimizes bias by ensuring a comprehensive search, focusing on relevant
articles and presenting a fair synthesis of findings. However, it is important to note that
bias can still exist in the literature itself; therefore, the studies were critically evaluated, and
any potential limitations or biases within the selected articles were acknowledged.
3. Results
3.1. The Impact of AI Chatbots on Technology Integration
Research Question 1: The development of AI chatbots has been claimed to herald a
new era, offering significant advances in the incorporation of technology into people’s lives
and interactions. Is this likely to be the case, and if so, where will these impacts be the most
pervasive and effective [3]?
The use of AI chatbots has the potential to bring significant advances and impact
various aspects of people’s lives and interactions [
17
], especially where human to human
interaction is not preferred or possible to obtain [
18
]. AI chatbots may provide customer
service and support, health care and mental health support, education and e-learning,
personal productivity and assistance, language translation and communication as well as
social companionship and entertainment [
19
,
20
]. The diversity of uses for AI chatbots and
the large mix of empirical literature means it is reasonable to focus on one area.
Mental health care is a good example, because AI chatbots have been considered a viable
resource in this domain for more than a decade [
21
]. There are promising clinical outcomes
for AI chatbots providing relevant and continually accessible
support [22,23]
for depression
in adults [
24
], anxiety in university students [
25
,
26
] and attention-deficit/hyperactivity
symptoms for adults [27].
Informatics 2023,10, 82 4 of 16
AI chatbots may help address the barriers to the help-seeking process for mental health
issues by offering personalized, accessible, affordable and stigma-free assistance, promoting
early intervention and generating valuable insights for research and policymaking [
28
–
31
].
AI chatbots may be particularly useful in monitoring, communication, memory assistance,
screening and diagnosis, with the aim of understanding a patient’s emotions and assisting
in the analysis of large datasets. For example, algorithms may identify patterns and trends
that might be missed by human analysts. By analysing a patient’s medical history, genetic
data and other relevant factors, algorithms could generate tailored symptom checks and
treatment recommendations that consider the individual’s unique needs and circumstances.
The opportunities for AI chatbots should also be considered in terms of the challenges
posed, such as a lack of human connection, a reliance on technology, the accuracy and
reliability of information, ethical and privacy considerations as well as misdiagnosis and
limited understanding [28–31].
A 2023 overview of mental health chatbots found 10 apps on the market for a variety
of mental health concerns (e.g., anxiety and depression) and users (e.g., rural dwellers, shift
workers, students, veterans and adolescents), for a variety of aims (e.g., to improve social or
job interviewing skills) [
18
]. The overview took interest in AI chatbots for their accessible,
affordable and convenient social and psychological support. However, vulnerable users
may overrate the benefits and encounter risks, especially during a crisis, because AI chatbots
were allegedly incapable of identifying crisis situations. Therefore, poor semantics were
found to undermine AI chatbots, because they were not developed enough to understand
the context of users’ words and failed to respond effectively or at all.
Users may not be aware of the difference between humans and humanlike chatbots.
These limitations are human factors, of which education is the key for effectively collabo-
rating to produce sustainable solutions [
32
]. Users and practitioners need guidance on the
uses of AI chatbots, similar to what is generally required for digital mental health platforms
and interventions [33].
The different fields of psychology, psychiatry, AI and health care, as well as educa-
tors, policymakers, computer scientists and technology developers working on mental
health care, means there are significant challenges to overcome in order to realize overall
benefits [
34
]. Mental health professionals and policymakers hold the key to AI chatbots
being a useful tool in the intelligent system toolbox. However, it appears that graduate
students and research scientists may best drive change through their willingness and ability
to effectively collaborate with computer scientists and technology developers.
AI chatbots offer promise as complementary tools rather than a replacement for human
mental health professionals [
18
,
20
]. A 2021 review of digital mental health interventions
(DMHIs) found AI chatbots to speculatively help mental health professionals meet over-
whelming service demand [
34
]. A 2023 systematic review and meta-analysis of randomized
controlled trials (RCTs) found AI chatbots to be acceptable for a wide range of mental health
problems [
35
]. For example, an RCT found a fully automated conversational agent, Woebot,
to be a feasible, engaging and effective way to deliver cognitive behavioural therapy (CBT)
for anxiety and depression in young adults [
25
]. There is promise for Woebot [
36
] and
Wysa [37] in establishing a therapeutic bond with users.
Although AI chatbots are feasible as an engaging and acceptable way to deliver therapy,
more studies are required for what may facilitate a digital therapeutic alliance [
36
,
37
]
and to reduce misunderstandings [
38
]. Mental health chatbot attrition rates are lower in
comparison to other digital interventions [
24
,
39
]. However, dropout rates require attention,
as does clarity around what disorders they are useful for [
40
]. Some reviews found a high
potential for AI chatbots in identifying patients at risk of suicide [
41
–
43
], and triage and
treatment development through NLP integrated to social media in real-time [44–46].
Developments in Generative Pre-Trained Transformer (GPT) programs like ChatGPT
4 means AI chatbots may be used in suicide prevention [
47
]. However, there is a need for
better understanding AI chatbot limitations such as negative sentiment, constrictive think-
ing, idioms, hallucinations and logical fallacies. A study of messages related to people’s
Informatics 2023,10, 82 5 of 16
suicidal thoughts sought insights from the arrangement of their words, the sentiment and
rationale [
48
]. While AI chatbot hallucinations and fallacies require human intervention, it
is possible to detect idioms, negative sentiment and constrictive language with off-the-shelf
algorithms and publicly available data. However, safety concerns were publicized after a
chatbot, Eliza, was blamed by a Belgian man’s widow for her husband’s suicide [49].
There is a need for qualitative studies to help reduce poor semantics and errors as well
as increase trust in AI chatbots. For example, thematic analysis from retrospective data is
required to identify common themes of messages sent to mental health chatbots in order
to increase the effectiveness of AI chatbots as a source of support. AI chatbots may help
improve problem areas through NLP for sentiment analysis, which is fast and effective
qualitative data analysis, to assist in understanding multidimensional online feedback.
Recommendations for identifying and evaluating the impact of AI chatbots are
as follows:
•
Conduct qualitative studies using AI chatbots to demonstrate how they assist with
accessibility, engagement and effectiveness through (1) identifying user needs,
(2) understanding
barriers to its use, (3) evaluating user experience and AI chatbot
impact and (4) integrating human–AI approaches to overcome problem areas.
•
Contribute to empirical evidence with longitudinal studies and RCTs to see which
mental health conditions and populations AI chatbots may be recommended for.
•
Determine a practical attrition prediction possibility to identify individuals at a high
risk of dropping out through applying advanced machine learning models (e.g.,
deep neural networks) to the leveraging analyses of feature sets (e.g., baseline user
characteristics, self-reported user context and AI chatbot feedback, passively detected
user behaviour and the clinical functioning of users).
3.2. The Balance between the Benefits and Harms of AI Chatbots
This is difficult to answer on a global scale because of a lack of widely collaborative
international standards in addition to the diversity of applications for AI chatbots. However,
current investment in AI research, education, societal adaptation, innovation, employment
opportunities and jobs creation appear to be insufficient upon considering the scale of the
impending changes.
The novelty and complexity of AI in mental health means it is timely to focus on
cutting-edge education such as specialized university courses in digital mental health
and informatics that use peer-reviewed and routinely updated textbooks and modules.
The intent should be to stimulate discerning skills and critical thought from a mix of
subjects that will assist in pioneering benefits to mental health care and AI technology
industries while also mitigating the increasing costs from mental illness. While AI chatbots
are eminent, they have yet to reach their potential in assisting with mental health problems
in digital users, who are predominantly young people [18].
Quality, effective and usable chatbots such as Woebot and Wysa are available to assist
with mental health support [
36
,
37
]. However, various studies are needed to show evidence
for a broader array of mental health disorders and symptoms. Furthermore, development
is mostly being driven by the technology side with an interest in mental health rather
than by mental health professionals who are technologically savvy. The differences in
communication styles and methodologies between technology and mental health care
researchers (i.e., pattern-based versus hypothesis-derived) has limited the combination of
these approaches. Another hindrance is the limited opportunities for high-level researchers
who are capable of understanding and implementing hybrid methods.
However, there is good potential for mental health care to serve as an example where
AI chatbots may assist in providing (cost-)effective solutions for a range of users and
aims [
21
–
24
,
36
–
38
]. Mental health care professionals may need to embrace AI chatbots
for their use to become more productive [
50
]. There also needs to be conscious efforts to
broaden the way in which productivity is measured if significant advances integrating
technology into people’s lives and interactions are to be realized. For example, how can AI
Informatics 2023,10, 82 6 of 16
chatbots’ contribution to the economy and the health of people be accurately measured?
While there may be a gain to the gross domestic product (GDP) of developed countries, there
may also be some job losses because of AI disruption. Although productivity, affordability
and accessibility are important levers, so are policies that consider mental health and
human capital.
The impact of AI chatbots on productivity needs to be considered in terms of national
and international economics, standards and regulations. It is apparent that not all gov-
ernments are aligned in principle. Also, the digital divide is questionable in terms of not
further marginalizing the underserved and the unserved [
34
]. Therefore, productivity and
humanity need to be considered in terms of global risks such as war and the costs that the
physical effects of climate change will bring [
50
]. While some governments heavily invest
in defence, the decarbonisation of heavy industries and transition among energy systems,
there will be competing demands for investment into AI technologies. Meanwhile, the
example of the emergence of ChatGPT shows the difficulties of stakeholders grappling
with the pace of technological development.
It is unclear how the Productivity Commission’s forecasts of AI boosting the Australian
economy have been calculated to arrive at a predicted boon between a 66.67% and 266.67%
higher GDP in the next decade [
7
]. In 2023, the Australian Government projected an outlook
for the next 40 years in terms of intergenerational equity, with forecasts of higher financial
burdens on younger generations [
51
]. This leads to the question of how such countries
manage and maximize the major shifts that are underway in their national economy while
also effectively integrating the impact of AI technologies.
In the example of mental health care in Australia, it is necessary to explore the existing
structure for safety and quality to see AI’s consistency with it before examining its economic
potential. Australia’s National Standards in mental health services provide a framework
for safety and quality in hospitals and community services and are primarily intended to
regulate the practice of mental health professionals [
52
]. Yet, with overwhelming demand
and limited supply in mental health care exacerbated by further strain during the COVID-19
pandemic [
53
,
54
], digital mental health filled a service gap, culminating in the development
of the National Safety and Quality Digital Mental Health Standards in 2020, which aimed
to improve the safety and quality of digital mental health service provision [
55
]. However,
mental health professionals and policymakers are currently confronting the opportunities
and challenges of AI [
56
]. For example, prompt engineering is used with ChatGPT to
bypass content filters in social media. This could lead to harm and problems such as
exploiting vulnerability.
The Australian Government adopted a voluntary ethics framework in 2018 for “re-
sponsible” AI, in order to guide businesses and governments to responsibly design, develop
and implement AI [
57
]. However, mainstream AI chatbots are mostly being developed in
the US. Australia and the US are among the various countries seeking input or planning on
AI chatbot regulation [
58
]. The EU implemented the Digital Services Act and the Digital
Market Act, aiming to create a safer digital space where the fundamental rights of users
are protected and to establish a level playing field for businesses [
59
]. There is a need to
ensure that AI algorithms are developed and trained using diverse and representative
datasets and that any insights generated by AI are rigorously validated and verified by
human experts. ChatGPT’s owners, OpenAI, suggested proactively managing the risks of
these “frontier AI” models [
60
]. OpenAI initially proposed conducting pre-deployment
risk assessments, external scrutiny of model behaviour, using risk assessments to inform
deployment decisions and monitoring and responding to new information about model
capabilities and uses post deployment.
It is essentially up to users to be transparent about their use of AI, take steps to
protect privacy and confidentiality and take care to use it responsibly for optimising its
performance [
61
]. For example, recruits may use AI chatbots to accomplish their duties,
which detracts from the intent of seeking human input and raises critical questions about
the value of cooperative work if trust cannot be established and maintained [
62
]. A
Informatics 2023,10, 82 7 of 16
main problem with AI chatbots is that they are a new technology with the potential of
becoming fundamentally pervasive in terms of cybersecurity risk because of their ability to
create all sorts of malicious codes and algorithms that can cause infrastructure or financial
system chaos [63].
The use of AI in mental health research has been well established as potentially
yielding important insights and improving outcomes for individuals with mental health
disorders [
64
,
65
]. However, it is important to carefully classify and regulate “high” risks
and prioritise ethical considerations at every step. The increasing use of AI chatbots for
mental health and crisis support means that stakeholders need to increase their attention
and education in order to effectively leverage these tools [
18
,
66
]. For example, fair aware
AI has been called for in digital mental health to promote diversity and inclusion [
67
],
and explainable AI has been suggested as a tool for demonstrating transparency and trust
between users and practitioners [68].
It is proposed that HAI may complement these concepts in an evolving AI system
where multiple AI models work together with human input to generate recommendations
and predictions rather than relying on a single algorithm. A next step is establishing
optimal combinations of humans and AI chatbots for various tasks in research, practice
and policy [
69
]. However, it is necessary to consider AI technologies overall in terms of
plans for broad-ranging regulation.
According to Australia’s AI Ethics Principles [
70
], regulation can help achieve safer,
more reliable and fairer outcomes for all Australians; reduce the risk of negative impacts
on those affected by AI applications; and encourage businesses and governments to prac-
tice the highest ethical standards when designing, developing and implementing AI. A
subsequent position statement on generative AI suggested that regulation can help ad-
dress concerns about potential harms such as algorithmic bias and errors, the spread of
misinformation, inappropriate content and the creation of deepfakes [71].
By implementing measures such as transparency, accountability and risk mitigation
strategies, regulation can help ensure that AI is used responsibly and ethically [
72
]. Fur-
thermore, regulation can help raise public trust in AI technologies by ensuring that they are
developed and used in a manner that is consistent with societal values and expectations [
73
].
This can help facilitate the adoption of AI technologies and enable society to fully realize
their potential benefits.
The regulation of AI should include defining what constitutes “unsafe” AI and de-
termining what aspects of AI should be subject to regulation [
74
]. This requires a clear
understanding of the anticipated risks and benefits of AI technologies on a global scale as
well as insights into the public’s trust and the acceptance of AI systems. While people in
Western countries are more cautious of AI and less confident that the benefits outweigh the
risks, those in the emerging economies (i.e., Brazil, India, China and South Africa) are more
trusting and embracing of AI, in addition to young, university-educated people as well as
those in managerial roles [75].
Overly stringent regulations could stifle innovation and hinder the development of AI
technologies [
76
]. As such, regulation requires international cooperation to be truly effec-
tive. Without a global consensus, companies might simply migrate their AI development
activities to less regulated jurisdictions, leading to a regulatory race to the bottom. There
is a need to secure AI models and their associated systems by using industry-standard
security protocols. AI models and systems should be regularly updated and patched to
address any discovered vulnerabilities.
Recommendations for regulating and/or promoting the responsible use of AI applica-
tions are as follows:
•
Invest in research to evaluate the efficacy and potential harms of AI applications and
develop systems to monitor and audit AI systems for unusual or suspicious activity.
•
Implement rigorous safety measures, robust regulations and collaborative standards
to ensure the responsible use of AI technologies.
Informatics 2023,10, 82 8 of 16
•
Validate a HAI model combining AI chatbots with human experts in research, practice
and policy to optimise mental health care assistance.
3.3. The Mitigation of Bias and Prejudice in AI Applications
The World Health Organisation offered a warning, stating that the use of generative AI
for health care must be approached with caution [
77
]. AI algorithms are only as good as the
data they are trained on, and biases in the data can lead to biased results [
78
]. Additionally,
the use of AI in mental health care raises important risks and ethical considerations [
79
] as
well as security, bias and privacy concerns, particularly when it comes to the storage and
use of sensitive medical and personal data [80].
More generally, there are “high-risk” uses of AI and automated decision-making,
which warrant the warning of potential harms, including the creation of deepfakes and
algorithmic bias [
81
]. There is also concern about AI perpetuating or amplifying biases or
narrow viewpoints [
82
,
83
] as well as automating jobs and therefore replacing humans in
some capacities [
84
]. However, AI can be used to counter disinformation and to improve
the accuracy and reliability of reporting [
85
,
86
]. The challenge lies in defining and deciding
what constitutes “unsafe” AI. Various Australian science experts have called for rigorous
safety measures, robust regulations and standards to be implemented for these “unsafe”
AI [
76
]. It is apparent that mitigating measures for high-risk AI should be quickly and
proactively sought to avoid hampering progress in AI.
Generative AI is being used in the media to create more personalised and targeted
advertising, to automate content creation and curation and to analyse audience behaviour
and preferences [87,88]. Misinformation or disinformation may stem from tools like Chat-
GPT [
87
] in combination with social media, which produce the mass de-prioritization of
legitimate news outlets in favour of spam as well as false or manipulative user-uploaded
content [
87
]. Bias and errors in generative AI [
67
,
87
] highlight the questionability of existing
information assessment guidelines because of evidence credibility, source transparency
and limitation acknowledgment. Generative AI has shown the need for new guidelines to
promote ethics, fairness, privacy and transparency [
76
] as well as recognize the intellectual
property rights of human creators and organizations [
89
]. This may be exacerbated by po-
tentially anticompetitive practices used by dominant technology platforms such as Google
and Meta [88].
There is a need to counter and correct the AI applications that help perpetuate bias,
harassment and marginalization as well as the loss of critical thinking and independent
thought. AI chatbots may be a part of innovative solutions to answer calls for the de-
tection and moderation of fake news [
90
] and the transparent regulation of social media
platforms [
91
–
93
]. As an example, a review of the impact of YouTube on loneliness and
mental health found its recommendation algorithms may inadvertently reinforce existing
beliefs and biases, spread misinformation and disinformation as well as enable unhelpful
or harmful content [
46
]. However, the review also found that YouTube can have positive
effects on loneliness, anxiety and depression if users actively engage with the platform and
use it as a tool for education, social connection and emotional support.
There are opportunities for biased and prejudiced AI applications to be countered and
corrected through education and research with the assistance of AI chatbots [
94
]. However,
human researchers/experts who understand the history and context of research problems
may need to help prompt and supervise AI chatbots for solutions. For example, YouTube’s
recommendation algorithm is designed to suggest videos based on users’ viewing history,
search queries and other data points [95]. Since YouTube’s launch in 2005 up until 2011, it
was designed to recommend videos that attracted the most views or clicks. Then, in 2012,
it was designed to directly respond to metrics such as shares, likes and, to a lesser extent,
dislikes. From 2016 onwards, it was designed to increase safety, with efforts made to remove
dangerous content and demonetise those who did not follow regulations. However, the
development of AI chatbots means that continuous adaptation is critical through legislation
and setting ethical values [94] in addition to improving current AI systems [46].
Informatics 2023,10, 82 9 of 16
YouTube has initiated mental health policies, algorithm changes, content moderation,
content creator and user psychoeducation, mental health and crisis resource panels, self-
harm and suicide content warnings and parental controls and settings [
96
]. YouTube
reported largely reduced borderline content as it immediately removes offensive content
where detected [
97
]. However, the algorithm can also create filter bubbles and echo
chambers where users are exposed to content that reinforces their existing beliefs and
biases [
98
]. This can lead to polarisation and misinformation, which can have negative
consequences for mental health. Improved algorithms are called for to detect bias and
errors as well as moderate how videos appear in a watch list in order to steer users to
safe, well-informed and inclusive content as well as refer them to mental health and crisis
resource panels with suitable information and resources, in conjunction with assistance
from AI chatbots [46].
However, problematic social media use in young people affects one in three indi-
viduals in the Australian youth, and it is not only limited to YouTube [
99
]. For example,
cyberbullying is also an issue in other social media (e.g., Facebook, Twitter, Snapchat,
Instagram, TikTok) [
100
]. Various studies have found a clear link between heavy social
media use and an increased risk for depression, anxiety, loneliness, self-harm and suicidal
thoughts [
101
–
103
]. Although there is a lack of psychological studies on TikTok [
104
], a
causal study across American colleges found that access to Facebook led to an increase in
severe depression by 7% and anxiety disorder by 20% [
102
]. This significant link between
the presence of Facebook and a decline in mental health in young people is concerning
when considering the fact that the arrival of Facebook in 2004 was followed by an increase
of 57% in deaths by suicide among Americans aged 10–24 between 2007 and 2017 [105].
Major data breaches and the use of “psychological warfare tools” on Facebook were
reported in 2018 with the Cambridge Analytica files [
106
]. After calls were sounded for
data to be used following principles of ethics, privacy and security, Australia took the
international lead in social media regulation with the Online Safety Act 2021 after public
hearings revealed that Facebook’s algorithms were potentially harmful and unsafe [
107
].
However, in 2022, the Australian government and the technology industry realized that
an outdated classification system hindered the creation of new codes for regulating online
content [
108
]. In 2023, Australia expressed interest in pursuing risk-based classification
systems for AI chatbots as were being drafted in Canada and the EU [109].
Advances in AI chatbots and other tools such as predictive models and virtual assis-
tants means that multiple models may be combined with human expert input to address
mental health challenges and suicide prevention, improve access to care and reduce the
barriers to seeking help. These tools use NLP and ML to mine mental health data, to
understand and respond to individuals’ needs and to provide personalised support. A
theoretical framework proposed an adaptive Social Media Virtual Companion (SMVC) for
educating and supporting adolescent students in interactions in social media environments
in order to achieve a measure of collective well-being [
110
]. This SMVC framework is an
example of how to design social media systems and embedded educational interventions
through HAI, because automatic processing powered by a recommendation algorithm is
combined with educator/expert intervention and guidance.
HAI mental health strategies are proposed to be useful for the design and devel-
opment of a multi-model responsible social media system in education settings. For
example, an adaptive SMVC may assist in promoting the obtaining of more balanced and
diverse content as well as reducing the impact of bias and errors in algorithmic recom-
mendation systems such as filter bubbles and echo chambers. By integrating off-the-shelf
solutions like Viable for sentiment analysis and DataMinr for monitoring and analysing
social media, the SMVC system can learn from HAI feedback and recent data to adjust
recommendations accordingly.
However, AI-generated sentiment affects the emotional language used in human
conversation, therefore potentially affecting social relationships. Randomized experiments
found that algorithmic recommendation systems change how people interact with and
Informatics 2023,10, 82 10 of 16
perceive one another socially; people are evaluated more negatively if they are suspected of
using an algorithmic response from AI chatbots like ChatGPT [
111
]. Therefore, educators
should proactively and transparently encourage the use of AI chatbots to avoid negative
perceptions. Users may need to be taught how to be discerning and critical of the infor-
mation AI chatbots provide and learn how to effectively leverage these tools to help solve
complex problems in their studies as well as cautiously use them in self-care for mental
health—obtaining assistance where required.
Recommendations for countering and correcting the flaws of AI applications are
as follows:
•
Vulnerable people need more informed guidance on how to self-manage their mental
health when assisted by AI chatbots in order to connect with resources and treatments.
•
Social media mental health and crisis resource panels may be enhanced by linking to
AI chatbots that provide vetted digital mental health and crisis services or referrals
as necessary.
•
HAI mental health strategies with SVMC may be explored for cautiously navi-
gating a safer, more responsible social media with humane, fair and explainable
system recommendations.
4. Conclusions
This narrative literature review has explored the multifaceted impacts of AI chatbots
on various aspects of society, particularly focusing on their potential in the field of mental
health care. This review is useful for providing an overview of the topic, identifying gaps
in the literature and generating new research questions. By synthesizing both theoretical
and empirical research, this study provided a comprehensive overview of the current state
of AI chatbots in mental health care. The evidence presented indicates that AI chatbots
hold promise for revolutionizing mental health support, offering accessibility, engagement
and effectiveness in assisting individuals and populations with a wide range of mental
health concerns and aims. However, it is crucial to approach their implementation and
regulation with caution and responsibility. The novelty of AI chatbots in mental health
means that this narrative literature review shows examples of theoretical and empirical
research that future studies can apply.
The development of AI chatbots brings opportunities for serving underserved and
unserved populations, as well as blending care for the well-served, especially in treating
common disorders such as anxiety and depression. However, there are challenges in know-
ing which AI chatbots are of good quality and which are useful and effective. Therefore, it
is important for future research to clarify these areas as well as the level of care required for
crisis support. The human factors of human–computer interaction require more attention
through empirical research. AI chatbots offer accessible and convenient support, helping to
address the barriers in the help-seeking process for mental health issues and have shown
promise in various clinical trials. Nevertheless, limitations such as poor semantics, biases
and the need for qualitative studies to improve user experience must be acknowledged and
addressed. AI chatbots should be seen as complementary tools rather than replacements for
human mental health professionals. Notwithstanding, there is a need for more empirical
evidence and advocacy for users and practitioners to distinguish the quality, usability and
effectiveness of AI as well as its uses and the populations that would benefit from them. If
AI chatbots evolve to provide appropriate answers to these areas for clarification, then a
more autonomous use of these tools will become progressively possible.
Furthermore, there is a need for regulation and responsible use of AI applications,
given the potential for biases, privacy concerns and the amplification of misinformation.
This emphasizes the importance of international collaboration in establishing standards
and regulations to ensure the ethical and transparent use of AI technologies. The balance
between innovation and regulation must be carefully struck in order to avoid stifling
progress while safeguarding against potential harm.
Informatics 2023,10, 82 11 of 16
Additionally, the review highlights the role of collaborative AI in countering biases
and errors in AI applications, especially in the context of social media and mental health
support. By integrating human expertise and sentiment analysis into AI models, it becomes
possible to provide more balanced and diverse content while reducing the impact of
algorithmic biases.
Overall, the review shows promise for the use of AI chatbots in mental health care,
but it also highlights the need for further research, such as scoping reviews, to evaluate
their effectiveness and address the risk of bias and ethical concerns. It emphasizes the
need for careful consideration, research and collaboration in harnessing the potential of
AI chatbots. While they offer transformative possibilities in various domains, responsible
development, regulation and ongoing evaluation are essential to maximize their benefits
while minimizing risks. Collaborative efforts between technology developers, mental
health professionals, policymakers, researchers and educators can help ensure that AI
chatbots contribute positively to society’s well-being and mental health support.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The author declares no conflict of interest.
Abbreviations
ACM Association for Computing Machinery
AI artificial intelligence
CBT cognitive behavioural therapy
DMHIs digital mental health interventions
EU European Union
GDP gross domestic product
GPT Generative Pre-Trained Transformer
HAI Human–artificial intelligence
HCI human–computer interaction
IEEE the Institute of Electrical and Electronics Engineers
ML machine learning
NLP natural language processing
RCT randomized controlled trial
UK United Kingdom
US United States
WHO World Health Organization
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