[4]Guanghui Fu

1]School of Software Engineering, Beijing University of Technology, Beijing,
China

2]School of Nursing, Wuhan University, Wuhan, China

3]School of Nursing, Joint Research Centre for Primary Health Care, The Hong
Kong Polytechnic University, Hong Kong, China

4]Sorbonne Université, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-
Salpêtrière, Paris, France

# A Generic Review of Integrating Artificial Intelligence in Cognitive
Behavioral Therapy

Meng Jiang  Qing Zhao  Jianqiang Li  Fan Wang  Tianyu He  Xinyan Cheng  Bing
Xiang Yang  Grace W.K. Ho<EMAIL_ADDRESS>[  [  [  [

###### Abstract

Cognitive Behavioral Therapy (CBT) is a well-established intervention for
mitigating psychological issues by modifying maladaptive cognitive and
behavioral patterns. However, delivery of CBT is often constrained by resource
limitations and barriers to access. Advancements in artificial intelligence
(AI) have provided technical support for the digital transformation of CBT.
Particularly, the emergence of pre-training models (PTMs) and large language
models (LLMs) holds immense potential to support, augment, optimize and
automate CBT delivery. This paper reviews the literature on integrating AI
into CBT interventions. We begin with an overview of CBT. Then, we introduce
the integration of AI into CBT across various stages: pre-treatment,
therapeutic process, and post-treatment. Next, we summarized the datasets
relevant to some CBT-related tasks. Finally, we discuss the benefits and
current limitations of applying AI to CBT. We suggest key areas for future
research, highlighting the need for further exploration and validation of the
long-term efficacy and clinical utility of AI-enhanced CBT. The transformative
potential of AI in reshaping the practice of CBT heralds a new era of more
accessible, efficient, and personalized mental health interventions.

###### keywords:

Artificial intelligence, Mental health, Cognitive behavioral therapy, Large
language model, Machine learning, Deep learning

##  1 Introduction

Mental health issues have become increasingly prevalent, especially in the
post-pandemic era [111]. Psychological distress brought on by COVID-19 has
added to the existing mental health crisis, leading to higher rates of
depression, anxiety, and suicidal ideation. Consequently, there is an urgent
need for effective and accessible mental health interventions. Cognitive
Behavioral Therapy (CBT) is widely recognized as one of the most important
psychological interventions for addressing a range of mental health issues
[49, 32]. As the gold standard for treating depression and anxiety, CBT has
been integrated into healthcare systems worldwide. Its primary goal is to
identify and restructure patients’ maladaptive cognitive frameworks to help
them develop coping skills, address behavioral issues, and alleviate symptoms.
However, the delivery of CBT, as it is traditionally conducted individually
and face-to-face by a therapist, faces barriers in real life, including social
stigma and limited access to qualified therapists, particularly in underserved
areas. Only 27% of those receiving psychological therapy actually access
standardized care [11]. To address these challenges, technological
advancements have led to the development of Computer-Based Cognitive
Behavioral Therapy (CCBT) and Internet-Based Cognitive Behavioral Therapy
(ICBT) [162]. These variations use computer software and the Internet as a
medium to deliver CBT interventions based on established theories and
techniques. Several established online CBT platforms, such as MoodGYM [9] and
30-Day Self-Service Psychological Expert [26], have emerged. These online
platforms have partially alleviated some of the issues related to the shortage
of mental health professionals and poor accessibility [27]. However, they
still face challenges, such as limited interactivity and high dropout rates.
In addition, they offer interventions that address only general cognitive
issues rather than individual specific needs. Given this challenge, there is
an urgent need for more flexible and adaptive forms of CBT to better meet the
diverse needs of different patient populations.

In recent years, rapid advancements in artificial intelligence (AI) technology
have led various industries to explore its innovative applications. AI’s
exceptional capabilities in data analysis, pattern recognition, and automation
offer vast potential in the delivery of mental health services, providing new
tools and methods for assessment, diagnosis, and treatment of mental health
conditions [78, 89, 34]. For instance, deep learning (DL) models can automate
mental health assessments and diagnostic processes, alleviating the workload
of professionals while enhancing the accuracy and efficiency of diagnoses
[156]. Furthermore, AI can also aid in detecting and understanding patients’
unique emotional expressions, thereby offering recommendations for
personalized psychological interventions and support [8]. Given the shortage
of skilled mental health professionals, innovative AI approaches have been
developed to guide peer-to-peer mental health support [133]. Additionally, AI-
based psychological counseling support systems, such as those utilizing large
language models (LLMs), have also been developed to assist junior counselors
in providing online psychological support [50]. These studies demonstrate that
AI not only expands the boundaries of traditional mental health services, but
also brings new opportunities for improving their accessibility and utility
globally [55, 35]. In the realm of CBT, the integration of AI has led to
revolutionary advancements, particularly with the rise of LLMs. Notable
research in this area includes the development of CBT-specific prompts and
tailored datasets, leading to models like CBT-LLM, which are specifically
designed for CBT delivery [99]. Other efforts have resulted in conversational
counseling agents based on LLMs, such as utilizing GPT-2 in CBT to generate
human-like textual narratives to provide psychological support [119],
providing psychological counseling using CBT techniques [79], and delivering
dialogue modules focused on Socratic questioning using LLMs like OsakaED and
GPT-4 [66]. These advancements have made cognitive behavioral interventions
more personalized and precise, better addressing the needs of diverse
patients.

Given the abundance of research emerging in this field, several review
articles have summarized innovative developments in CBT. However, the exsiting
reviews focused mainly on CBT applications [64, 36] and the development of
ChatGPT in redesigning CBT for different age groups and genders [22]. There is
a lack of comprehensive review summarizing the application of AI’s role across
various stages of CBT delivery. To address this, we provide a detailed
literature review of AI’s role in enhancing CBT in this paper.

##  2 Background

Cognitive behavioral therapy (CBT) stands as a structured, time-limited
psychological treatment that focuses on the interplay between cognitive
processes, emotional responses, and behavioral patterns. This therapeutic
approach operates on the premise that thoughts, emotions, and behaviors are
interconnected, and aims to enhance psychological well-being by addressing
maladaptive cognitive patterns and behaviors [15]. The theoretical foundation
of CBT encompasses both cognitive and behavioral aspects. Regarding the
origins of CBT, there are varying views in the academic community. However, it
is generally accepted that the popularization of the “cognition” began with
Ellis’s Rational Emotive Therapy (RET) in the 1950s, followed by Beck’s
Cognitive Therapy (CT) in the 1960s. Since then, CBT has continuously
integrated various behavioral therapies and theories, evolving into the widely
used psychological treatment seen today.

CBT interventions typically target three domains: cognition, behavior, and
emotion [93]. In the cognitive domain, CBT focuses on individuals’ cognitive
processes, including thoughts, beliefs, and interpretations. This involves
aiding clients in identifying and understanding negative or distorted thought
patterns, such as negative automatic thoughts and cognitive distortions (CD)
(e.g., jumping to conclusions, all-or-nothing thinking and mental filtering).
Techniques like cognitive restructuring (CR) are used to adjust these
patterns, fostering a more objective and positive perspective toward oneself
and the world. In the behavioral domain, CBT addresses individuals’
maladaptive behavioral patterns and habits. Therapists collaborate with
clients to explore their unhealthy behaviors, such as social withdrawal,
avoidance, and substance or alcohol misuse. Techniques such as behavioral
experiments, graded exposure, and behavioral activation (BA) are then
employed. Further, CBT acknowledges the interplay between the body and the
mind. Physiological responses such as tension, fear, and anxiety can impact
cognition and behavior. Thus, CBT also focuses on regulating physiological
responses through techniques such as deep breathing, progressive muscle
relaxation, and physical activity to enhance emotional regulation and
psychological well-being. For the emotional domain, CBT targets clients’
emotional awareness and ways to manage them by aiding clients to recognize
negative emotions as they arise, accurately identify them, and utilize
appropriate cognitive and behavioral strategies to support better emotional
well-being. The three domains are intertwined, and CBT typically tailors a
range of techniques and strategies based on the individual’s specific
circumstances and needs, aiding in problem-solving, improving mental health,
and building resilience [40].

CBT has been employed to address a variety of mental health issues, including
anxiety disorder [103], depression [154], schizophrenia [153], attention
deficit and hyperactivity disorder [107], insomnia [16], eating disorders
[85], bipolar disorder [41], substance use disorders [94], and obsessive-
compulsive disorder [97]. Beyond mental health, CBT has been explored as a
tool for managing chronic health conditions like low back pain [114, 59],
asthma [108], and tinnitus [108]. Prior to initiating CBT, therapists
typically conduct assessments to determine symptom severity and treatment
goals. These assessments may involve dialogue between therapist and client or
self-administered tools like the Beck Depression Inventory (BDI-II) or the
Beck Anxiety Inventory (BAI) [12, 14, 48]. Based on this assessment,
therapists work with the clients to establish treatment goals and offer
personalized cognitive and behavioral strategies tailored to their needs.

##  3 Artificial intelligence in CBT

Integrating AI into CBT presents a myriad of potential applications, promising
to enhance the effectiveness and utility of therapy. To provide a summary of
the extant literature on the integration of AI into CBT across the delivery
process, we conducted a literature review by performing searches on the ArXiv
and Google Scholar database without date restrictions. Search terms considered
in this review were selected based on two elements, CBT (e.g., cognitive
behavioral therapy, cognitive distortions, cognitive restructuring, and
exposure therapy) and technology (e.g., artificial intelligence, machine
learning, deep learning, natural language processing, large language model,
chatbot, and virtual reality). Articles that enhance, support, or implement
CBT through AI technology will be selected. Results were categorized based on
the stage at which AI was integrated in the CBT delivery process and were
synthesized. In this section, we present the synthesized findings describing
the role of AI in different stages of the CBT treatment process, as
illustrated in Figure 1.

To be clear, this article only covers the stages of CBT that current AI
technology has addressed. Due to the complexity of CBT treatment, some details
and specific treatment stages have not yet been covered by AI, and thus are
not discussed here. Future research and technological advancements may further
expand AI’s role in CBT delivery, encompassing more stages of the treatment
process.

Figure 1: The role of AI in various stages of CBT treatment.

###  3.1 Integration of AI in the Pre-Treatment Stage

A comprehensive assessment is a foundational step in all psychological
treatments, including CBT. This initial assessment involves gathering
information on the client’s history, current issues, and therapeutic goals
through structured or semi-structured interviews, questionnaires, and
standardized scales. In CBT, particular emphasis is placed on assessing the
client’s cognitive and behavioral patterns, as well as their emotional
responses. The insights from this detailed assessment guides therapists in
crafting tailored CBT treatment plans that effectively address the client’s
concerns, thereby maximizing therapeutic outcomes. Traditional CBT assessment
methods, while thorough, often rely heavily on manual processes, which can be
subjective and time-consuming. The reliance on the therapist’s clinical skills
and professional acumen can may also limit diagnostic accuracy and treatment
efficiency. In contrast, AI technology, with its advanced data processing and
analysis capabilities, offers a powerful alternative. AI can rapidly analyze
vast amounts of client data to identify patterns and correlations, thereby
assisting therapists in assessing patient symptoms more accurately and
swiftly, and in identifying adverse emotions and cognitive distortions.

####  3.1.1 Mental health assessment

##### Disease diagnosis and symptom severity assessment

AI is increasingly employed for the diagnosis and assessment of common mental
illnesses such as depression and anxiety. [157] proposed a methodology to
support the diagnosis of mental disorders in psychiatric emergency context
using vocal acoustic analysis and machine learning, targeting disorders like
major depressive disorder, schizophrenia, bipolar disorder, and generalized
anxiety disorder. [140] employed a survey questionnaire distributed among
Bangladeshi students with a total of 684 responses were obtained, and utilized
ten machine learning models and two deep learning models to predict three
levels of depression severity (normal, moderate, and extreme) with remarkable
accuracy. [156]introduced the Trans-CNN, which effectively integrates the
strengths of Transformer and Convolutional Neural Network (CNN) architectures.
This model demonstrates superior performance in identifying depressive
disorders compared to traditional CBT methods and standalone Transformer or
CNN models by analyzing textual data such as patient narratives, treatment
records, and diagnostic reports.

Despite these advancements, the existing research is centered around AI models
focused on the diagnosis and assessment of singular psychological disorders,
with limited research dedicated to the development of comprehensive models
capable of simultaneously diagnosing multiple co-occurring psychological
conditions.

##### Identifying cognitive distortions

CBT postulates an individual’s emotions and behaviors are influenced by their
perception and interpretation of events. Cognitive distortions refer to
erroneous or irrational patterns of thinking that can lead to negative
emotions and maladaptive behaviors. During the initial assessment phase of
CBT, therapists collaborate with clients to explore these cognitive patterns
and reactions to specific events, aiming to assist the client in identifying
and understanding potential cognitive distortions, which were categorized into
ten types by Burns theory [19]. They include: all-or-nothing thinking, over
generalization, mental filter, disqualifying the positive, jumping to
conclusions, magnification and minimization, emotional reasoning, should
statements, labeling and mislabeling, and blaming oneself or others.

Currently, AI technology facilitates the identification of cognitive
distortions primarily through text classification techniques. Researchers
utilize various textual data as inputs to build models and algorithms that
automatically identify and categorize these cognitive distortions. However,
due to the lack of publicly available structured datasets specifically
designed for detecting cognitive distortions, researchers often turn to
alternative data sources such as social media data [5, 159], personal blogs
[141], and everyday narratives [166, 136, 98]. [138] compared five
classification algorithms for detecting cognitive distortions using a
therapist Q&A dataset obtained from Kaggle. [150] applied NLP methods to
identify cognitive distortions from text messages exchanged between patients
with severe mental illnesses and their clinical therapists. Challenges in this
task include dealing with short texts that lack contextual information and
imbalanced data where certain distortion types are underrepresented, leading
to poorer classification performance. To address these issues, researchers
have proposed various solutions. [5] suggest enriching short textual
representations to improve cognitive distortions’ classification of the Arabic
context over Twitter. It also utilizes a transformer-based topic modeling
algorithm (BERTopic) that employs a pre-trained language model (AraBERT). [39]
tackled data imbalance with approaches like data augmentation and domain-
specific models, demonstrating the effectiveness of the pretrained language
model MentalBERT. Recent advances have expanded the scope of cognitive
distortion detection to include multimodal datasets. This cross-modal research
approach provides a more holistic perspective, enabling a more comprehensive
detection of cognitive distortions. For instance, [142] proposed a multitask
framework that integrates text, audio, and visual data to detect cognitive
distortions, achieving significant performance improvements over existing
state-of-the-art models.

Recently, LLMs have also shown promise in complex psychological tasks such as
identifying cognitive distortions [117, 100, 25, 83]. [117] conduct
experiments to compare LLMs and supervised learning in cognitive distortion
identification and suicide risk classification. The experimental results
indicate that LLMs struggle with accurately identifying complex cognitive
distortions in Chinese social media data, suggesting that deep learning
algorithms remain the preferred solution for complex psychological tasks like
cognitive distortion identification. [100] developed TeaBot, an AI tool fine-
tuned on GPT-3, and employs CBT techniques to aid users in identifying and
challenging distorted thoughts. [25] introduced the Diagnosis of Thought (DoT)
framework, which strategically prompts the LLMs to produce diagnosis
rationales pertinent for cognitive distortions detection. Although the DoT
method demonstrates its capability in classifying cognitive distortions, it
also exhibits a notable flaw, namely, the model tends to over-diagnose
cognitive distortions even when the user’s statements are benign. [83]
addressed this by introducing the ERD framework, involving extraction,
reasoning, and debate among multiple LLM agents to classify cognitive
distortions from user utterances. This approach significantly mitigates the
issue of overdiagnosing cognitive distortions in the DoT method.

##### Emotion analysis

Emotion analysis is essential for understanding individuals’ emotional states.
This understanding enables therapists or conversational agents to offer more
empathetic support and feedback in CBT interventions, thereby strengthening
the therapeutic alliance and enhancing the interactive experience [18, 116].
Furthermore, emotional states also serve as indicators of intrinsic goals,
observable behaviors, and treatment efficacy. During the therapeutic process,
individuals are more likely to benefit from treatment when they can
effectively manage their emotions [95]. Therefore, during the initial
assessment phase, therapists explore the clients’ emotional responses and
assist them acquire healthier emotion regulation strategies. AI-assisted
emotion analysis has become increasingly prevalent in mental health [148, 8,
74]. [116] employed an emotion mining algorithm to assess the overall
sentiment and five specific emotions expressed in texts written by patients
during Internet-based cognitive behavioral therapy (ICBT), finding moderate
agreement between the algorithm and human judgment in evaluating the overall
sentiment, while the agreement was low for specific emotions. [109] proposed
an intelligent social therapeutic chatbot. This chatbot defined several basic
emotion labels, and based on these emotion labels, three deep learning
algorithms were employed to extract emotions from user chat data. [75]
introduced Terabot, a conversational system that enhanced sentiment and
emotion recognition by integrating CBT techniques and replacing BERT with
RoBERTa in a neural language model framework. However, [145] pointed out that
some emotion recognition methods [45, 116] categorize emotions into discrete
classes, failing to capture the continuum and diversity of emotions
accurately. To address this, they proposed a deep learning-based dimensional
text emotion recognition system within the context of CBT using the ALBERT
pre-trained model [76], fine-tuned on emotion-annotated data for dimensional
score prediction.

In recent years, ChatGPT, as a prominent example of conversational AI, shows
potential in assisting individuals with emotion understanding and management.
[121] initially assessed GPT’s capability for overall sentiment analysis
(positivity, negativity, or neutrality) in English and Arabic texts. They
found that GPT demonstrates effective multilingual sentiment analysis,
achieving performance levels comparable to top-performing machine learning
models from previous years. Furthermore, they examined GPT’s ability to
accurately discern more nuanced discrete emotions such as anger, joy, fear,
and sadness. The results demonstrated a high level of consistency between
GPT’s performance and human judgement. [43] also underscored GPT’s superiority
over humans in emotional cognition, as highlighted in their study.

Despite AI’s capabilities in emotion detection and understanding, there
remains a perception that individuals may perceive AI-driven support as
lacking genuine emotional resonance compared to human interaction [168]. Thus,
future research should focus on making AI responses more conversational and
human-like to address this perception. Additionally, to ensure rigor and
utility in real clinical settings, human cross-validation is required.

####  3.1.2 Personalized treatment selection: identifying CBT beneficiaries

Within the realm of precision medicine, aligning clients with the most
suitable treatment is a critical and significant objective [106]. This
involves selecting an effective treatment plan before commencing therapy,
thereby avoiding the risks associated with ineffective treatments. CBT is a
well-established psychotherapy approach to address mental health issues,
however, its effectiveness varies among individuals experiencing different
mental health conditions. This variability highlights the need to identify
which clients are likely to respond favorably to CBT and to assess its
effectiveness in treating specific mental health conditions. Researchers are
increasingly exploring the potential of AI models to predict which clients may
benefit from CBT and to determine the most appropriate treatment for each
person [10, 124, 155]. Studies suggest that AI can analyze extensive clinical
data to forecast which patients are more likely to respond positively to CBT.
This capability can assist clinicians in selecting suitable candidates for
CBT, thereby optimizing resource allocation. For example, [124] used machine
learning with cross-validation to investigate the potential of functional
connectivity patterns to predict obsessive-compulsive disorder (OCD) symptom
severity in individuals after treatment. The findings have important clinical
implications, particularly in the development of personalized medicine
approaches aimed at identifying OCD patients who would derive the greatest
benefit from intensive CBT. Similarly, [155] conducted a comprehensive review
and quantitative synthesis of previous studies utilizing machine learning to
predict the outcomes of CBT treatment in various diagnostic categories. They
concluded that, on an individual level, the accuracy of predicting CBT benefit
was approximately 74.0%.

Besides CBT, other types of psychological interventions, such as Psychodynamic
Therapy (PDT) and person-centered counseling for depression may be equally
effective. Moreover, not all clients may require high-intensity CBT
interventions. Some individuals might benefit from low-intensity
interventions. Traditionally, the trial-and-error method in psychotherapy can
be both time-consuming and costly, with the risk of clients undergoing
multiple ineffective treatments before finding the one that works best for
them. This not only delays effective care but also increases the overall cost
of treatment. Therefore, determining the most suitable treatment for specific
individuals before the delivery of therapy is crucial. Addressing this gap,
[33] and [130] had applied machine learning algorithms to recommend optimal
treatment approaches based on patients’ pre-treatment characteristics. This
method can contribute to substantial cost savings in mental health care,
making high-quality psychotherapy more accessible and affordable.

Additionally, there is a growing interest in whether combining CBT with
medication or other treatments can enhance outcomes. Several researchers have
explored this possibility [56, 110, 143]. For example, [56] found that
combining CBT with medication is equally effective and more long-lasting than
medication alone. Furthermore, they noted that the combination of medication
and CBT could increase response rates and prolong the duration of
effectiveness, especially when CBT is administered to clients responsive to
pharmacotherapy.

These research underscores the potential of AI in refining the selection of
psychological treatments and highlights the importance of personalized
approaches in enhancing therapeutic outcomes.

####  3.1.3 Client psychoeducation

In CBT, client education is a crucial step both before and throughout the
therapy process, as shown in Figure 1. Before commencing therapy,
psychoeducation involves introducing the fundamental principles and techniques
of CBT, aiding them in understanding common patterns of negative emotions,
thoughts, and behaviors and coping strategies [93]. In the treatment process,
psychoeducation is reflected in teaching clients how to identify and assess
automatic thoughts, how to perform cognitive restructuring, and how to find
alternative explanations for automatic thoughts, thereby adjusting these
automatic thoughts. By conveying that individuals can acquire and master
skills to address psychological issues, psychoeducation enhances clients’
sense of self-efficacy and can even contribute to symptom reduction on its own
[30].

Psychoeducation can be delivered through various formats, including written
materials, videos, audio recordings, as well as organizing specialized
lectures and seminars. The advent of mobile applications has introduced novel
avenues for delivering psychological education and support, capitalizing on
their convenience and adaptability. Psychological education has been
seamlessly integrated into several mobile applications, some of which have
demonstrated efficacy in treating various mental disorders, including stress
[127], anxiety and depression [58], eating disorders[21], post-traumatic
stress disorder [123], and obsessive-compulsive disorder [164]. For instance,
[164] developed the smartphone application “Mayo Clinic Anxiety Coach”,
designed to deliver CBT for anxiety disorders, including OCD. This application
comprises a psychological education module that instructs users on the
utilization of the application, conceptualization of anxiety, descriptions of
various anxiety disorders, explanations of CBT, and guidance on assessing
alternative forms of therapy. Similarly, [92] developed a mobile-based
embodied conversational agent (ECA) for the prevention and detection of
suicidal behavior. The ECA introduces users to a segment of psychoeducational
video in which a combination of cartoon-like images in motion are accompanied
by a narrator explaining the basic elements of CBT. Users can access the video
at any time through the application’s main menu. The advantages of delivering
psychological education through mobile applications are self-evident:
information becomes portable, and patients can access it easily. [61]
integrated CBT within immersive gaming experiences to assist individuals
suffering from generalized anxiety disorder (GAD). In this approach, CBT
elements are seamlessly interwoven with the diegetic components of the game,
providing psychoeducation in an engaging and entertaining manner.

AI-powered models facilitate personalized psychoeducation by tailoring modules
to each patient’s understanding and preferences. For example, Bhaumik et al.
[17] introduced MindWatch, a system harnessing AI-driven language models for
early symptom detection and personalized psychological education. Within the
psychological education module, they utilized the foundational Llama 2 model
within the Amazon SageMaker Studio environment to deliver tailored education
to individuals experiencing mental health issues. Numerous chatbots
incorporate modules for psychoeducation as well. For instance, [69] developed
Todaki, a chatbot for managing Attention Deficit Hyperactivity Disorder
(ADHD). This chatbot offers tailored psychoeducation and brief CBT sessions,
enabling individuals with ADHD to acquire self-help skills for managing their
condition effectively. Similarly, [146] created XIAO AN, an AI-assisted
psychotherapy robot utilizing multi-modal signal recognition and natural
interaction technology. XIAO AN monitors emotions and offers brief,
comprehensive psychological therapy based primarily on CBT, integrating
psychological education modules.

Through these approaches, integrating CBT principles into various
technological platforms has facilitated the dissemination of psychological
education, empowering individuals to better understand and manage their mental
health.

###  3.2 Integration of AI in CBT therapeutic process

CBT intervention encompasses the implementation of various strategies,
including cognitive restructuring, behavioral activation, and exposure
therapy, etc. These strategies are intrinsically connected to cognition,
emotions, and behavior. During CBT treatment, psychotherapists select
appropriate strategies based on the patients’ symptoms and needs to provide
targeted treatment. In section 3.2.1, we will delve into how AI technology can
enhance each CBT strategy. Following this, Section 3.2.2 will focus on how
researchers are developing chatbots to integrate multiple CBT strategies,
offering comprehensive mental health support to users. Next, Section 3.2.3
introduces how wearable devices or smartphone applications equipped with AI
technology can now be used to monitor patients’ psychological states during
CBT treatment. Finally, Section 3.2.4 introduces some researchers utilize AI
to make early predictions about the outcomes of CBT treatments.

####  3.2.1 AI Augmentation of Therapeutic Strategies for CBT delivery

##### Cognitive restructuring (CR)

Cognitive restructuring (CR) is a fundamental component of CBT, characterized
as a structured, goal-oriented, and collaborative intervention. It involves
the patient and therapist working together to identify and correct irrational
thoughts, evaluations, and beliefs by generating more adaptive alternative
cognitive patterns [28]. Thereby alleviating negative emotions, improving
mental health, and promoting healthier behaviors and coping strategies. To
some extent, it is considered analogous to Cognitive Reappraisal [139, 169].
Recent advancements have leveraged AI models to conduct CR effectively. The
majority of these efforts aim to provide evidence that AI language models can
effectively engender reframed thinking in response to negative emotional
situations, as well as to compare the efficacy of different models in CR. For
instance, [151] collected a small-scale CBT dataset via the crowd-sourcing
platform Prolific. They validated Google’s T5 transformer and BERT model for
their ability to transform initial negative cognition into more positive or
realistic alternatives. Human evaluation revealed that T5-generated
reconstructions resembled original human-written responses more closely, while
BERT showed a relatively lower performance but higher positive sentiment. [87]
introduced PATTERNREFRAME, a novel dataset that incorporates personas and
classical unhelpful thought patterns, extending the reframing task to include
the identification and generation of thoughts corresponding to a given persona
and unhelpful pattern. They evaluated various language models using prompt-
based and fine-tuning methods for their efficacy in identifying and reframing
these thoughts. Unlike [151] and [87], who conceptualize CR tasks as a
sentence rewriting task, [165] emphasize client empowerment over reliance on
therapist-driven solutions. They propose, Helping and Empowering through
Adaptive Language in Mental Enhancement (HealMe), a model utilizing Large
Language Models (LLMs) for CR through three steps: distinguishing between
situations and thoughts for a rational perspective, generating alternative
perspectives through brainstorming to alleviate negative thinking, and
providing suggestions that recognize the client’s efforts and promote positive
action.

Previous research primarily focused on the perspective of holistic CR,
attempting to address how language models can be utilized to generate CR.
However, [135, 134] have begun to delve deeper into exploring various
dimensions of CR, and examining people’s preferences for particular types of
restructuring. To be more specific, [135] investigated how negative thinking
can be restructured, how language models can be utilized to facilitate such
restructuring, and which types of restructuring individuals experiencing
negative thoughts prefer. They introduced a framework comprising seven
linguistic attributes for reconstructing thoughts and trained a retrieval-
enhanced in-context learning model to generate reconstructed thoughts.
Additionally, they conducted a randomized field study on the Mental Health
America (MHA) website. In another work, [134] designed and evaluated a CR tool
based on human language model interaction. This system leverages language
models to assist individuals in various steps of CR, including identifying
cognitive traps within thoughts and selecting more actionable, empathetic, or
personalized reconstruction suggestions when reconstructing negative thoughts.
Furthermore, they demonstrated that language models can not only be utilized
for generating reassessments but also aid individuals in enhancing their own
reassessment abilities. [161] argue that while [135, 134] explored the
enhancement of reframed thoughts within a single attribute in one generation,
their efforts were limited in addressing multiple features. Consequently, they
developed ReframeGPT, leveraging GPT-3 as an inference engine to generate and
iteratively refine reframed thoughts across various features, aiming to
achieve high-quality reframing. [82] focused on aligning GPT-4’s
reconstructive thinking with human performance, enhancing model performance in
CR tasks by understanding the differences between human and AI-generated
reconstructions.

Identifying a patient’s automatic thoughts and emotions is crucial for
effective CR. In traditional face-to-face CBT sessions, therapists refine
identified automatic thoughts through methods such as Socratic questioning.
However, when delivering CR in ICBT, accurately capturing thoughts and
emotions poses challenges. Hence, [51] trained the T5 model to predict
emotions associated with each automatic thought. They then compared these
predictions with judgments from relevant experts, demonstrating the accuracy
of T5 predictions. The application of T5 in iCBT platforms holds promise for
achieving more efficient CR. [137] and [71] also have both underscored the
significance of automatically identifying patients’ cognitive distortions and
emotions in CR. In response to this, corresponding efforts have been made.
Particularly, [71], drawing on the ABCD model in CBT, employed the pre-trained
model ERNIE 3.0 to construct a hierarchical text classification model for
extracting automatic thoughts and emotions from user statements. Furthermore,
they also validated the efficacy of LLMs in this task.

##### Behavioral activation (BA)

Behavioral activation (BA) is a essential therapeutic strategy in CBT,
focusing on addressing observable behaviors for intervention [113]. In BA,
therapists collaborate with clients to formulate specific goals and plans,
encourage the modification of negative behavior patterns, gradually increase
the frequency and diversity of positive activities, and help clients regain a
sense of pleasure and meaningful engagement in daily life. [81] and [67]
demonstrated that behavioral activation achieves outcomes comparable to full
CBT and is comparable to pharmacotherapy [38]. Individuals with mental disease
may have problems with reduced daily activities, loss of interest, avoidance
behavior and social avoidance. To facilitate the application of BA,
researchers have developed innovative interventions.

Innovative interventions have been developed to facilitate BA application.
[31] developed a Spanish-language behavioral activation self-help mobile
application called ¡Aptívate!. Within this app, the user identifies
individualized values and associated activities across five life areas,
including relationships, daily responsibilities, recreation, career and
education, and health. Evaluation results also demonstrated the feasibility
and preliminary effectiveness of this app. [126] developed a mobile health
system called MUBS (Mobile-based Behavioral Activation System) to support
patients with depression through behavioral activation therapy. The system
implements a unique content-based probabilistic recommendation algorithm to
motivate patients to engage in positive behaviors by providing activity
directories and personalized rewards, helping them build awareness of
behavioral activation.

While these studies primarily focus on evaluating the efficacy of BA or
developing applications to assist, prompt and motivate patients to actively
engage in various activities, without explicitly linking AI to BA. Against
this background, [88] and [122] explore how AI technology can be utilized to
enhance the effectiveness of behavioral activation. [88] proposed a novel
approach for activity recognition utilizing AI. They employed multi-modal data
(combining speech and text) and utilized a BERT model for emotion recognition,
along with logistic regression for sentiment detection. Subsequently, an
activity classification model was employed wherein emotion and sentiment were
integrated with keywords to accurately identify the appropriate activity for
behavioral activation. This method achieved accuracies exceeding 80% in
emotion recognition, sentiment detection, and activity recognition tasks.
[122] designed and developed a chatbot named Bunji using AI technology to
provide emotional support, personalized behavior activation, and remote health
monitoring. Participatory evaluation in a pilot study environment also
demonstrated the practicality and effectiveness of the chatbot.

##### Exposure therapy (ET)

Exposure therapy, a common technique in CBT, involves gradually exposing
patients to anxiety-provoking or phobias-provoking situations to help them
adapt and reduce their fear responses [2]. It is used to treat various
psychological disorders, including specific phobias, panic disorder, post-
traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), and
social anxiety disorder. In recent years, virtual reality (VR) technology has
emerged as a potent adjunct to traditional exposure therapy [68], leading to
the development of Virtual Reality Exposure Therapy (VRET), validated by
research for its effectiveness within CBT [131, 91, 105].

While VRET may not yield superior results in terms of therapeutic efficacy
compared to traditional exposure therapy within the CBT framework, it presents
unique advantages like enhanced patient comfort, safety, real-time
customization of exposure content, and simulation of personalized real-life
scenarios. The integration of VR with CBT has led to the development of
interactive applications and serious games to augment therapeutic
interventions. [61] developed ReWIND, an RPG-styled serious game based on the
ABCDE model of CBT. The design intention of ReWIND is to immerse players
diagnosed with symptoms of Generalized Anxiety Disorder (GAD) in various
anxiety-inducing situations within a virtual environment and provide them with
constructive psychological interventions. The game aims to deliver
psychological education to players in an engaging and interactive manner.
Similarly, [53] emphasize that CBT is the most successful protocol in gambling
disorder (GD) treatment, and they developed a serious virtual game called
Alter Game, based on VRET, aimed at preventing relapse in patients with
gambling disorder. Additionally, [96] proposed an Android application named
CBT Assistant, where patients can choose, set, and customize their own
exposure fear ladders based on a generic template for graded exposure therapy.
This feature distinguishes CBT Assistant from other CBT applications. The
fusion of VRET with AI technology further enhances its efficacy in addressing
mental health challenges. [4] proposed and discussed the idea of utilizing
Explainable Artificial Intelligence (XAI) to enhance CBT treatment for speech
anxiety in VR settings. [118] explore a range of machine learning models’
performance on the task of arousal prediction using publicly available
datasets. They propose a pipeline to address the model selection issue with
various parameter configurations in the context of VRET.

##### Homework

A CBT session typically lasts 45-60 minutes, which is often insufficient for
many patients. Homework assignments bridge the gap between sessions, allowing
patients to apply learned skills and enabling therapists to assess skill
acquisition and maintenance [13, 77]. In the professional literature, homework
assignments are frequently delineated as precise, structured therapeutic tasks
upon during sessions, intended for completion between sessions. This process
entails collaborative delineation of therapeutic objectives for the homework,
determining pertinent activities or data collection as components of the
homework, strategizing the practical execution of the homework, and
subsequently reviewing the homework during subsequent sessions [73]. Homework
assignments may encompass a range of activities within each session, such as
engaging with relevant materials, documenting thoughts and emotions,
practicing specific skills or behaviors, or engaging in communication with
others [149].

Research has found that clients who consistently complete homework derive
greater benefits from interventions compared to those who complete little or
no homework [20], yet traditional paper-based CBT homework pose various
impediments that can significantly undermine users’ motivation to complete
tasks as instructed. Non-compliance with homework is cited as one of the most
common reasons for the failure of CBT treatments [60], persisting as a
prevalent issue in clinical practice. The prevalence of digital devices and
the internet has enabled the transformation of traditional homework into
digital formats, thereby enhance CBT homework compliance. However, there is a
lack of guidelines for designing mobile phone apps tailored for this purpose.
Consequently, [149] proposes six essential features of an optimal mobile app
aimed at maximizing CBT homework compliance, aiming to provide theoretical
guidance for the development of such applications.

Innovative approaches, such as the integration of traditional diary writing
with mobile technology and LLM, offer promising solutions to enhance homework
compliance. For example, [112] developed a machine learning model capable of
recognizing the presence of homework assignments during therapy sessions based
on natural language dialogue between therapists and clients in real-world
settings, as well as determining the type of homework assigned. Such
advancements hold significant promise in bolstering therapists’ ability to
assign and monitor homework tasks, ultimately fostering enhancements in
therapeutic outcomes. [101] integrated traditional diary writing with mobile
technology and LLM to create a diary application with contextual awareness,
named MindScape. Specifically, the application utilizes real-time analysis of
behavioral data collected from smartphones and employs LLM to provide
personalized, contextually relevant writing prompts. These prompts are
designed to guide users in reflection and contemplation, facilitating the
recording of their thoughts within daily life contexts. This innovative
approach not only fosters a habit of regular self-reflection but also
addresses the challenge of homework compliance in CBT. During CBT for tinnitus
alleviates the patients are typically assigned various homework tasks,
including diary writing and self-monitoring. These homework assignments
primarily consist of handwritten text data. However, analyzing this data can
be extremely time-consuming for therapists, leading to decreased treatment
efficiency. To address this issue, [70] proposed utilizing LLMs like GPT-2 to
analyze the homework data of patients undergoing CBT. Their goal is to predict
the Tinnitus Handicap Inventory (THI) scores from the homework, which can, in
turn, predict the outcomes of CBT treatment, thereby enabling the selection of
more personalized and effective treatment plans. Additionally, they compared
the performance of the latest language models, particularly Google’s T5 and
Flan-T5, in predicting THI scores. Finally, they looked ahead the application
of this research to monitor and predict the effectiveness of CBT treatment in
patients with depression.

In summary, AI augments various CBT strategies by leveraging natural language
processing and machine learning techniques, improving effectiveness and
engagement.

####  3.2.2 Developing AI tools for comprehensive delivery of CBT

While CBT strategies are often discussed individually, in clinical practice,
they are typically combined to address the specific needs of clients. Recent
advancements in AI have fostered the creation of digital tools that utilize
the principles and techniques of CBT. These tools integrate multiple CBT
strategies, thus providing a comprehensive approach to treatment. AI-powered
chatbots, also referred to as conversational agents or relational agents
exemplify such innovations, with chatbots being particularly notable.
According to a review by Abd et al. [1], out of 17 chatbots offering
psychotherapy, 10 were based on CBT. They are capable of mimicking the
communication styles of human therapists by engaging users in interactive
dialogues that include both verbal and non-verbal exchanges. This allows them
to offer efficient, personalized, and readily accessible CBT treatment,
available 24/7. Pioneering chatbots like Woebot, Wysa, Tess, and Youper are
emblematic of this trend. Their innovative approaches and effectiveness have
garnered significant attention and recognition as effective psychological
support systems based on CBT. In recent years, in response to diverse needs,
researchers have developed many new AI tools based on CBT principles [104,
125, 120, 128, 146, 102]. As summarized in Table LABEL:tab:ai_cbt_tools, we
have outlined specific AI tools currently utilized in CBT interventions

Table 1: AI tools used in current CBT intervention AI tools |  Description
---|---
Woebot111Woebot: https://woebothealth.com/ |  Woebot is a chatbot that offers CBT-based therapy for depression and anxiety. It engages users in daily conversations, tracks their emotions, and introduces CBT concepts through short videos or interactive word games. Using decision trees and natural language processing, Woebot responds empathetically and provides helpful suggestions. It can also detect concerning language and directs users to external resources if needed.
Wysa222Wysa: https://www.wysa.com/ |  Wysa is a therapy chatbot designed to support mental health issues like depression, anxiety, stress, and loneliness. It utilizes CBT, mindfulness, and positive psychology techniques. Instead of AI-generated responses, Wysa uses pre-crafted therapeutic conversations developed by clinicians for safe and effective interactions. Its adaptive AI understands complex user inputs and offers empathetic feedback and tailored CBT-based tools.
Youper333Youper: https://www.youper.ai/ |  Youper is a chatbot that delivers CBT through three steps: a personalized mental health assessment, instant support via conversations, and symptom monitoring. It uses a decision tree to select responses, conducts real-time emotion analysis, and provides CBT interventions based on the user’s emotional state.
Tess444Tess: https://www.cass.ai/x2ai-home |  Tess is a psychological AI chatbot providing brief conversations for mental health support, psychoeducation, and reminders. It uses clinician-prepared statements to deliver interventions based on user-reported moods. Tess adjusts its responses based on user feedback, favoring CBT-based interventions for positive reactions and offering alternatives for neutral or negative ones. The platform is customizable to align with specific treatments or user demographics.
BetterHelp555BetterHelp: https://www.betterhelp.com/ |  BetterHelp is an online therapy platform that uses AI to match patients with licensed therapists and offers various approaches like CBT and psychodynamic therapy.
Rumi666Rumi: https://www.facebook.com/rumibot.bot/ [104] |  Rumi is a chatbot uses Rumination-focused Cognitive Behavioral Therapy (RFCBT) to explore the relationship between thoughts, feelings, and actions, aiming to improve mental health and reduce depressive and anxious symptoms.
Cloud Bot [125] |  Cloud Bot is a chatbot that utilized NLP technology to function as a psychologist. It focuses on applying a cognitive restructuring CBT technique to address users’ issues.
Saarthi [120] |  Saarthi is a chatbot using NLP and AI for delivering CBT and remote health monitoring to people with mental health issues. It offers real-time, evidence-based treatment that is accessible, affordable, and convenient, aiming to reduce anxiety and depression symptoms and enable long-term mental health monitoring.
SchizoBot [102] |  SchizoBot is a chatbot using artificial neural networks to deliver CBT for managing schizophrenia, aiding clinicians and ensuring consistent therapy administration for patients.
XIAO AN [146] |  XIAO AN is a Chinese AI psychotherapy robot designed to monitor emotions and provide effective therapy, primarily using CBT principles. It has shown effectiveness in treating anxiety disorders in clinical trials without replacing therapists.
Emohaa [128] |  Emohaa is a Chinese conversational agent, which consists of two platforms: one is template-based (CBT-Bot) for structured conversations and exercises based on Cognitive Behavioral Therapy principles, while the other (ES-Bot) allows for open-ended discussions on emotional issues and provides emotional support.
Table 1: AI tools used in current CBT intervention (continued)

By offering efficient, personalized, and readily accessible CBT treatment,
these AI agents provide round-the-clock support to individuals undergoing
therapy, which to some extent helps alleviate the pressure of insufficient
medical resources. Nevertheless, it’s worth noting that given the sensitivity
of mental health support, it is crucial to assess the chatbots before
presenting them to users.

####  3.2.3 Real-time monitoring and feedback

Traditional CBT often lacks real-time monitoring capabilities, relying solely
on self-reported data from clients. However, the advent of mobile health
technologies offers new possibilities for monitoring client health outside CBT
sessions. According to research by [167], CBT is well-suited for mobile
platform applications, which facilitate real-time monitoring of user states
and provide valuable data to therapists. Consequently, numerous mobile
applications have been developed to deliver CBT treatment and track users’
psychological states in real-time. [3] noted that loneliness can be a risk
factor for depression. To address this issue, the MoodTrainer application was
developed, which tracks users’ locations and isolating behaviors in real-time
and provides CBT interventions when it detects relevant behaviors. Also, [96]
introduced an Android application named CBT Assistant. This APP analyzes input
data from individuals with social anxiety disorder (SAD) to identify stressors
or situations triggering their mental health issues and assesses their
severity. Additionally, some mobile applications are designed for specific CBT
treatment scenarios, such as continuously tracking the sleep patterns of
individuals with sleep disorders [129] or providing cessation monitoring and
CBT examples to smokers [6], thereby enhancing their success rates. These
mobile applications leverage the capabilities of mobile devices to collect
real-time data and provide personalized interventions and support to
individuals undergoing CBT treatment.

In recent years, wearable devices and smartphone applications equipped with AI
technology are emerging as a trend for monitoring patients’ psychological
states. During CBT process, AI algorithms can monitor stress levels, physical
activity, speech changes, and other indicators to detect variations in a
patient’s psychological state. These algorithms can send timely alerts to both
patients and healthcare providers. [52] embedded accelerometer sensors into
smartphones to detect stress levels using classification algorithms like naive
bayes and decision trees, achieving 71% accuracy. Additionally, the long-term
collection and analysis of large datasets can help therapists and patients
gain a deeper understanding of the patient’s mental health patterns. With the
assistance of AI, therapists can access more comprehensive information,
enabling them to discern trends in mental health, identify triggers, and
evaluate treatment efficacy. These insights are crucial for making informed
therapeutic decisions and designing personalized interventions. [54] collected
physiological and movement data from wrist-worn biosensors in 20 adolescents
diagnosed with ASD. They developed prediction models utilizing ridge-
regularized logistic regression. These models demonstrate high accuracy in
forecasting instances of aggressive behavior towards others occurring within
the subsequent minute. Such advancements lay the groundwork for proactive
behavioral interventions and timely adaptive intervention systems in the
future.

Despite the growing use of AI-equipped wearable devices and mobile
applications for monitoring psychological states and data collection, there is
relatively limited research specifically focused on their application to CBT,
particularly considering the unique demands of real-time interaction inherent
in this therapeutic approach.

####  3.2.4 Early prediction of treatment outcomes

Even among those for whom CBT proves effective, there may be a subset with
limited benefits, potentially leading to a misallocation of both client time
and limited healthcare resources. Hence, predicting treatment outcomes early
in the therapeutic journey assumes critical importance, particularly to
prevent the misallocation of client time and healthcare resources. Recent
advancements in AI and large-scale data analysis have enabled the development
of models capable of forecasting individual responses to CBT with high
accuracy. [57] applied a novel machine learning approach to predict
individual-level response to CBT using fMRI data in patients diagnosed with
panic disorder and agoraphobia (PD/AG). Similarly, [152] utilze the machine
learning methods to predict how people will respond when offered CBT for
psychosis. Their two-step methodology involved first identifying potentially
predictive regions and then developing a model based on these regions to make
individual-level predictions. In a large-scale study, [72] analyzed data from
over 6000 patients undergoing Internet-delivered CBT (ICBT). They evaluated
the accuracy of various machine learning algorithms in predicting treatment
outcomes and explored the integration of these algorithms into an Adaptive
Treatment Strategy. [65] further examined the clinical utility of machine
learning in predicting ICBT outcomes. They investigated the optimal timing
within the treatment process for the model’s predictive accuracy to support
adaptive treatment strategies, proposing an optimal predictive model and
offering specific recommendations based on their comprehensive analysis.
Despite advancements, traditional predictive models often exhibit limited
accuracy, particularly in assessing the effectiveness of treating adolescent
social anxiety. Under this background, [172] addressed this by employing deep
learning techniques to construct a predictive model for the correlation
between CBT and adolescent social anxiety, showcasing significantly improved
predictive accuracy and reduced complexity compared to traditional models.
[115] seeks to develop a state-of-the-art deep-learning framework for
predicting clinical outcomes in ICBT by leveraging large-scale, high-
dimensional time-series data of client-reported mental health symptoms and
platform interaction data.

Numerous researchers have also explored the use of specific treatment outcome
predictors and potential biomarkers associated with particular diseases. For
instance, [163] utilized the Hamilton Depression Rating Scale (HDRS) score as
the primary outcome measure to investigate symptom changes in subjects
undergoing CBT. Employing machine learning algorithms, they developed a
support vector regression model, ultimately identifying left dorsolateral
prefrontal cortex (DLPFC) Regional Homogeneity (ReHo) as a neuroimaging
biomarker for the therapeutic effects of CBT in depression. Many previous
studies have relied on highly selective samples to predict the outcomes of
CBT. However, few have utilized routine available socio-demographic and
clinical data to accomplish this task. Therefore, [62, 63] applied machine
learning methods to clinical and socio-demographic data to predict the mental
health treatment outcomes of individual patients. Their findings suggest that
using routine data alone can feasibly predict treatment outcomes for mental
disorders, with accuracy significantly surpassing chance levels.

These studies collectively highlight the promise of integrating advanced AI
methodologies with clinical practice to enhance the early prediction of CBT
treatment outcomes, offering potential pathways for more tailored and
effective therapeutic interventions.

####  3.2.5 Evaluation of therapists and clients in CBT treatment

The application of AI in CBT extends beyond the diagnosis and assessment of
disorders, it also encompasses crucial evaluations of therapists and clients
by analyzing conversation data during therapy sessions.

##### Therapist treatment quality assessment

Given the prevalence of mental health issues, ensuring the quality of
psychotherapy is crucial to addressing the growing mental health demands and
the complexities of the social environment. Traditionally, quality assessment
is performed by human evaluators who listen to therapy recordings and review
therapy notes to assess specific therapeutic skills. However, this approach is
costly and time-consuming, resulting in limited feasibility and hindering
widespread implementation in practical settings. To address these challenges,
some researchers and technology developers have begun exploring the use of
automated techniques to monitor the quality of psychotherapy. AI offers
automated solutions for assessing therapy sessions and monitoring treatment
fidelity of CBT sessions [23]. For example, [44] used a large-scale dataset
containing session transcripts from more than 14000 patients receiving
internet-enabled CBT (IECBT) to train a deep learning model to automatically
categorize therapist utterances according to the role that they play in
therapy, generating a quantifiable measure of treatment delivered. The closer
the content provided by the therapist aligns with standard CBT protocols, the
more positively it correlates with significant symptom improvement in
patients. Conversely, the quantity of content unrelated to therapy shows a
negative association. This method allows for the indirect assessment of the
efficacy of CBT psychotherapy provided by the therapist. However, [46]
emphasize that, for CBT, the most commonly utilized coding scheme is the
Cognitive Therapy Rating Scale (CTRS), which defines a set of 11 session-level
codes reflecting skills and techniques specific to the intervention.
Consequently, they introduced a model for quality assessment of psychotherapy
sessions based on adapted BERT representations of therapy language use. Their
analysis focused on the binary classification of CBT sessions concerning the
overall CTRS score. [24] also utilized CTRS scores to assess the quality of
CBT sessions. However, unlike [46], they proposed a hierarchical framework for
automatically evaluating the quality of transcribed CBT interactions. [7]
proposed an approach that explicitly focuses on control-affine dynamical
system models. They attempted to extract local dynamic modes from short
windows of conversation and learn to correlate the observed dynamics with CBT
competence. Furthermore, some studies aim to identify areas where the
therapist excels and areas where improvement is needed by analyzing recordings
of therapy sessions, comparing therapists’ language and behavior against
standards of specific therapeutic models [144, 47, 171, 160]. This process
aids therapists in enhancing their professional skills, thereby improving the
overall quality of therapy. Particularly, [160] developed PATIENT-${\Psi}$, a
novel patient simulation framework for CBT training. Specifically, they
constructed diverse patient profiles and corresponding cognitive models based
on CBT principles, and used a large language model to act as a simulated
therapy patient. This role-playing therapeutic scenario helps mental health
trainees practice CBT skills.

##### Predictive analysis of clients treatment adherence

Treatment adherence refers to the extent to which clients actively engage in
and comply with the advice and instructions provided by healthcare
professionals, thereby adhering to the treatment regimen, and it directly
impacts the effectiveness and outcomes of the treatment [37]. Therefore,
client adherence evaluation is essential components of effective healthcare
delivery. Particularly, against the backdrop of ICBT, there has been a
reduction in face-to-face interactions between healthcare professionals and
clients, which may lead to low client engagement and high dropout rates. AI
models can analyze client behavior and responses during therapy sessions to
evaluate their understanding, engagement, and adherence to treatment content.
[29] presents a minimally data-sensitive approach, based on a self-attention
deep neural network, to perform adherence forecasting of clients undergoing
G-ICBT. This study leverages between 7 to 42 days of user-interaction data
(login/logout) from the eMeistring platform. This analysis can identify
individuals who may be at risk of early dropout from intervention. With this
information, clinicians can implement meaningful and targeted interventions
such as reminders, scheduling direct interactions, or modifying the treatment
approach to prevent premature termination.

###  3.3 Integration of AI in Post-treatment Stage

####  3.3.1 Long-term treatment response and relapse prediction

Since current CBT interventions typically have relatively short durations,
leading to inadequate durability of effects for some patients post-treatment,
with a considerable proportion experiencing relapse after treatment cessation.
Hence, predicting individual long-term clinical responses and relapse risks
remains a significant challenge. AI presents a promising avenue for predicting
relapse risks and enhancing the durability of CBT’s effects in patients
undergoing CBT. [90] proposed the first study using the multivariate SVM–fMRI
method to successfully predict long-term treatment response of ICBT for social
anxiety disorder, achieving a predictive accuracy reached 92% (95% confidence
interval 73.2–97.6). [86] utilized machine learning techniques, specifically
the XGBoost algorithm, to analyze follow-up data of patients undergoing Low-
Intensity Cognitive Behavioral Therapy (LiCBT). They developed a dynamic
prediction tool capable of identifying cases with higher relapse risk at four
distinct time points during the patients’ treatment journey, with the ability
to dynamically adjust prediction accuracy. This tool’s capacity for early
identification of high-relapse cases, combined with targeted relapse
prevention measures, can significantly enhance the long-term efficacy of LiCBT
in situations where psychological resources are limited.

In conclusion, the integration of AI in CBT holds promise for revolutionizing
the delivery and efficacy of psychological interventions. By leveraging AI
technologies throughout various stages of the treatment process, clinicians
can enhance treatment outcomes, personalize interventions, and optimize
resource allocation, ultimately improving mental health outcomes for
individuals on a global scale.

##  4 Datasets

Datasets play a crucial role in driving research at the intersection of CBT
and AI, providing foundational material for training, testing, and validating
AI algorithms, and furnishing practitioners and researchers with the bedrock
for constructing and evaluating CBT models. This section aims to review
existing publicly datasets relevant to the application of CBT and AI. In the
context of disease diagnosis and assessment tasks, numerous datasets have
already been extensively reviewed in various comprehensive articles.
Consequently, we will not elaborate on these datasets further in this paper.
Moreover, it is worth noting that for the task of selecting personalized
treatment strategies, there is a noticeable lack of publicly available
datasets. This paper will not cover these datasets in detail. Instead, we
focus on datasets for specific CBT-related tasks such as identifying and
classifying cognitive distortions, conducting cognitive restructuring, and
analyzing CBT conversation data. For clarity, datasets where descriptions are
unclear or ambiguous in the literature will not be discussed in this paper.

Table LABEL:tab:simplified_version_of_cd_dataset provides a comprehensive
overview of datasets used for detecting and classifying cognitive distortions.
As can be seen from this table, most of the cognitive distortion data sets are
in English, and fewer are in Chinese. Moreover, these datasets present several
notable challenges: First, low reliability. The labeling criteria vary
significantly across different datasets, leading to inconsistencies. Moreover,
the inherently subjective nature of labeling cognitive distortions further
compromises reliability. Second, there is a pronounced issue of data
imbalance, with certain classes of cognitive distortions lacking adequate
representation. This imbalance hampers the model’s ability to generalize well
across all classes. Table 3 summarizes datasets relevant to cognitive
restructuring. Additionally, Table 4 outlines datasets that involve CBT
conversations. For these areas, high-quality, annotated datasets are
particularly scarce for both cognitive restructuring and CBT conversation
analysis.

Table 2: Cognitive Distortions Dataset. For a clean presentation, we have only shown the simplified version of the cognitive distortion dataset, see Appendix A for more details. Study |  Description
---|---
[159] |  Size of Dataset: A total of 1644 data entries across 11 types of cognitive distortions, and 2000 entries for normal cases. Language: Not reported. Data Modalities: Text.
[42] |  Size of Dataset: A total of 34370 samples across 14 types of cognitive distortions. Language: English. Data Modalities: Text.
[136] |  Size of Dataset: Dataset CrowdDist contained 7,666 texts across all 15 distortions, with an average of 511 responses per distortion. Dataset MH contained two subsets: MH-C was annotated with 15 cognitive distortion labels with 1164 distorted texts, and the MH-D dataset was annotated with binary distorted/non-distorted labels, distorted for 1605 texts, not distorted for 194 texts. Language: English. Data Modalities: Text.
[83] |  Size of Dataset: A total of 2530 samples across 10 types of cognitive distortions. Language: English. Data Modalities: Text.
[138] |  Size of Dataset: A total of 3000 samples, 39.2% were marked as not distorted, while the remaining were identified to 10 type of distortions. Language: English. Data Modalities: Text.
[151] |  Size of Dataset: A total of 200 samples across 14 types of cognitive distortions. Language: English. Data Modalities: Text.
[135] |  Size of Dataset: A total of 1077 samples across 13 types of cognitive distortions. Language: English. Data Modalities: Text.
[87] |  Size of Dataset: About 10k samples across 10 types of cognitive distortions. Language: English. Data Modalities: Text.
[158] |  Size of Dataset: 7,500 cognitive distortion thoughts across 7 types of common cognitive distortions. Language: Chinese. Data Modalities: Text.
[117] |  Size of Dataset: A total of 3407 posts across 12 types of cognitive distortions. Language: Chinese. Data Modalities: Text.
[99] |  Size of Dataset: A total of 22,327 samples across 10 types of cognitive distortions. Language: Chinese. Data Modalities: Text.
|
Table 2: Cognitive Distortions Dataset (continued) Table 3: Cognitive Restructuring Dataset. Study |  Tasks |  Dataset source |  Description
---|---|---|---
[135] |  Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction. |  Thought Records Dataset and Mental Health America (MHA) website. |  Size of Dataset: A total of 300 situations-thoughts pairs, reframed thoughts per situation. Component: situation; thought; reframe; thinking_traps_addressed. Language: Chinese. Data Modalities: Text. Open Source: Yes111 [135]: https://github.com/behavioral-data/Cognitive-Reframing.
[84] |  Detection of Cognitive Distortion and cognitive restructuring. |  The corpus labeling utilizes one specialized opensource dataset, the Chinese psychological Q&A dataset PsyQA [147]. |  Size of Dataset: A total of 1900 sentences. Component: original text and reconstruction text. Language: Chinese. Data Modalities: Text. Open Source: Yes222 [84]: https://github.com/405200144/Dataset-of-Cognitive-Distortion-detection-and-Positive-Reconstruction.
[137] |  Identification and evaluation of automatic thoughts and cognitive restructuring. |  Recruit participants. |  Size of Dataset: Not reported. Language: Japanese. Data Modalities: Text. Open Source: Yes. It can be obtained by sending an email.
|  |  |
Table 4: CBT session dataset. Study |  Tasks |  Dataset source |  Description
---|---|---|---
[80] |  Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models. |  Crowdsourced Reddit posts of mental health from [132]. |  Size of Dataset: Three levels of empathy strategies, and the number of pairs for each strategy was as follows: “emotion reaction”=1,047, “exploration”=481, and “interpretation”=1,436. Language: English. Data Modalities: Text. Open Source: Yes111 [80]: https://github.com/behavioral-data/Empathy-Mental-Health.
[99] |  Enhance the precision and efficacy of psychological support through LLMs |  Get PsyQA Questions [147], which is derived from the Chinese online mental health support forum Yixinli, then utilizes CBT Prompt to generate CBT answers. |  Size of Dataset: 22,327 entries, each comprising questions, descriptions, and CBT responses. Language: Chinese. Data Modalities: Text. Open Source: Yes. Follow the data copyright protocols and obtain it by email. Note: The questions in the dataset originate from online mental health forum, and the responses are generated by ChatGPT, not professionals.
|  |  |

##  5 Discussion

The integration of AI into CBT has led to significant advances in pre-
treatment assessment, the therapeutic process, and post-treatment follow-up.
First, AI has improved efficiency by assisting with pre-treatment screening
and diagnosis, reducing therapist workload, and enabling real-time adjustments
to treatment plans through predictive capabilities. Second, AI has enabled
more personalized CBT by analyzing rich patient data and identifying subtle
patterns, enabling customized treatment plans that go beyond the traditional
reliance on therapist expertise alone. Finally, AI-powered CBT platforms have
increased the accessibility of mental health services by providing remote and
cost-effective treatment options through online platforms and mobile
applications that offer 24/7 support. However, there are several limitations
to current AI applications in CBT. In pre-treatment assessment, while AI
excels in diagnosing psychological disorders and assessing cognitive
distortions and emotional states using textual data, it has fewer applications
in analyzing video, audio, and behavioral data. The potential of multimodal
data for comprehensive diagnosis remains largely untapped. During the
therapeutic process, AI enhances individual CBT strategies but struggles to
cover the complexity of interventions comprehensively. AI tools like chatbots
and virtual therapy assistants vary in quality and lack standardized
development, leading to inconsistencies. Moreover, there are inadequate
metrics for evaluating feasibility, engagement, and satisfaction, complicating
platform comparison and improvement. Most AI-driven wearable devices and
mobile applications focus on general indicators like heart rate and activity
levels, lacking specialized tools for monitoring CBT-specific indicators. In
post-treatment, while AI aids in predicting long-term treatment responses and
relapse risks, challenges include variability in prediction data and
insufficient research on utilizing predictive results to formulate
personalized intervention strategies.

The future of AI in CBT holds great potential. Autonomous learning and
adaptive therapy systems can emulate human CBT therapists, engaging in multi-
round interactions with patients and adjusting strategies based on real-time
feedback. Group intelligence support and decision-making systems can improve
both therapist guidance and patient outcomes by aggregating the experience of
experienced practitioners and facilitating intelligent social support.
Cognitive augmentation and assistive systems can develop personalized tools to
enhance cognitive function, thereby increasing the effectiveness of CBT.
Customized, personalized CBT models can adapt to users’ specific data, such as
social background, culture, education, and environment, to provide tailored
responses and interventions that increase therapeutic effectiveness and user
satisfaction. Despite the potential of AI, several challenges need to be
addressed. Data security and privacy are paramount, requiring compliance with
privacy regulations, anonymization of sensitive information, and advanced
encryption techniques. Ethical and algorithmic bias must be mitigated by
ensuring data diversity, involving multiple stakeholders in development, and
continuously monitoring AI systems. Model explainability and transparency are
essential for responsible AI decisions, requiring methods to improve
interpretability and the use of rigorously tested models. Over-reliance on AI
is a risk because the success of CBT depends on the therapist-patient
relationship, which AI cannot replicate. AI should be a complementary tool,
not a replacement. Evaluation of models in clinical practice is necessary to
assess real-world effectiveness, acceptance, trust, and usability, taking into
account training costs and impact on medical practice. In summary, while AI
offers promising enhancements to CBT, it must be used responsibly and
ethically, complementing the guidance of professional therapists. The ultimate
goal is to use technology to support, not replace, human-centered mental
health care.

##  6 Conclusion

In this paper, we have conducted a comprehensive literature review of the
integration of AI technology into CBT . We explored the application of AI
throughout the CBT process, highlighting its significant transformative impact
and existing limitations. Subsequently, We have summarized publicly available
datasets relevant to various CBT-related tasks to provide a foundation for
future research. We suggested future research directions and acknowledged the
practical challenges that AI faces in clinical settings. Overall, our review
illuminates the multifaceted integration of AI in CBT, highlighting its
potential while providing a nuanced understanding of its capabilities. We hope
that the findings will guide future research, bring new perspectives to
clinical practice, and contribute to the advancement of mental health care.

Acknowledgements This work was supported by grants from the National Natural
Science Foundation of China (grant numbers:72174152, 72304212 and 82071546),
Wuhan University Innovation and Entrepreneurship Projects for College Students
(No: 202410486100), Fundamental Research Funds for the Central Universities
(grant numbers: 2042022kf1218; 2042022kf1037), and the Young Top-notch Talent
Cultivation Program of Hubei Province. Guanghui Fu is supported by a Chinese
Government Scholarship provided by the China Scholarship Council (CSC).

## Declarations

  * •

Competing Interests: The authors have no competing interests to declare that
are relevant to the content of this article.

##  Appendix A Cognitive distortion dataset

In this section, we provide full detail of the dataset as described in Section
4.

Table 5: Cognitive Distortions Dataset. Study |  Tasks |  Dataset source |  Description
---|---|---|---
[159] |  Cognitive distortion classification. |  Published research papers on cognitive distortions, examples from articles on the web that introduce cognitive distortions and social media posts. |  Size of Dataset: Before expansion: There were a total of 353 data entries for all cognitive distortions, and 1000 entries for normal cases. After expansion: There are now a total of 1644 data entries across 11 types of cognitive distortions, and 2000 entries for normal cases. Label: 12 labels, including 11 cognitive distortions and no cognitive distortions. Language: Not reported. Data Modalities: Text. Open Source: Yes. However, the article says it will share, but no link is given.
[136] |  Detection and Classification of Cognitive Distortions. |  Dataset CrowdDist, comes from the popular crowdsourcing platform Mechanical Turk(MTurk). Dataset MH, comes from TAO Connect, an online mental health therapy service. |  Size of Dataset: Dataset CrowdDist contained 7,666 text responses across all 15 distortions, with an average of 511 responses per distortion. Dataset MH contained two subsets: The MH-C dataset was annotated with 15 cognitive distortion labels with 1164 distorted texts, and the MHD dataset was annotated with binary distorted/non-distorted labels, distorted for 1605 texts, not distorted for 194 texts. Label: situation; thought; reframe; thinking_traps_addressed. Language: English. Data Modalities: Text.
[42] |  Detection and Classification of Cognitive Distortions. |  Facebook Empathetic Data, Twitter Data and Crowd-Sourcing. |  Size of Dataset: A total of 34370 samples. Label: 14 labels. Language: English. Data Modalities: Text. Open Source: Yes. However, the article says it will share, but no link is given.
[83] |  Detection and Classification of Cognitive Distortions. |  Dataset Therapist Q&A comes from crowd-sourced data science repository, Kaggle. |  Size of Dataset: A total of 2530 samples. Label: 10 labels. Language: English. Data Modalities: Text. Open Source: Yes111 [83]: https://www.kaggle.com/datasets/sagarikashreevastava/cognitive-distortion-detetction-dataset.
[138] |  Automatic Detection and Classification of Cognitive Distortions. |  Dataset Therapist Q&A comes from crowd-sourced data science repository, Kaggle. |  Size of Dataset: A total of 3000 samples, 39.2% were marked as not distorted, while the remaining were identified to have some type of distortion. Label: 11 labels, including 10 cognitive distortions and no cognitive distortions. Language: English. Data Modalities: Text. Open Source: Yes. However, the article says it will share, but no link is given.
[151] |  Convert negative or distorted thoughts into more realistic alternatives. |  A variety of sources such as CBT books, forums and public content aggregators. |  Size of Dataset: A total of 200 samples. Label: 14 labels. Language: English. Data Modalities: Text. Open Source: Yes222 [151]: https://github.com/itoledorodriguez/cbt-dataset.
[135] |  Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction. |  Thought Records Dataset and Mental Health America (MHA) website. |  Size of Dataset: A total of 1077 samples. Label: 13 labels. Language: English. Data Modalities: Text. Open Source: Yes333 [135]: https://github.com/behavioral-data/Cognitive-Reframing.
[87] |  Training models to generate, recognize, and reframe unhelpful thoughts. |  Obtain a diversity of contexts, situations and thoughts from PERSONACHAT dataset [170], and ask crowdworkers to rewrite them. |  Size of Dataset: About 10k examples of thoughts containing unhelpful thought. Label: 10 labels. Language: English. Data Modalities: Text. Open Source: Yes444 [87]: https://github.com/facebookresearch/ParlAI/tree/main/projects/reframe_thoughts. However, this article says it will be shared and gives a github link, but it does not contain data.
[158] |  Cognitive Distortion Detection and investigate the association between cognitive distortions and mental health. |  Carefully select and train volunteers to observe scenes and note possible cognitive distortions, then have experts evaluate the results. |  Size of Dataset: 7,500 cognitive distortion thoughts. Label: 7 common cognitive distortions. Language: Chinese. Data Modalities: Text. Open Source: Yes555 [158]: https://github.com/bcwangavailable/C2D2-Cognitive-Distortion.
[117] |  Classification of Cognitive Distortions. |  Comments on a Sina Weibo post by the user ”Zoufan”. |  Size of Dataset: A total of 3407 posts. Label: 12 labels. Language: Chinese. Data Modalities: Text. Open Source: Yes666 [117]:https://github.com/HongzhiQ/SupervisedVsLLM-EfficacyEval.
[99] |  Enhance the precision and efficacy of psychological support through LLMs. |  Get PsyQA Questions [147], which is derived from the Chinese online mental health support forum Yixinli, then utilizes CBT Prompt to generate CBTanswers. |  Size of Dataset: A total of 22,327 samples. Label: 10 labels. Language: Chinese. Data Modalities: Text. Open Source: Yes. Follow the data copyright protocols and obtain it by sending an email. Note: The questions in the dataset originate from online mental health forum, and the responses are generated by ChatGPT, not professionals.
Table 5: Cognitive Distortions Dataset (continued)

##  Appendix B Abbreviation

In this section, as shown in Table 6, we summarize the full names and
abbreviations of various specialized terms mentioned throughout the text. By
providing this list of abbreviations, our aim is to assist readers in quickly
referencing and understanding the meanings of these terms, thereby enhancing
comprehension of the content and its context within the paper.

Table 6: Full name and abbreviation of terms Abbreviation | Full Name
---|---
ADHD | Attention Deficit Hyperactivity Disorder
AG | Agoraphobia
AI | Artificial Intelligence
AR | Augmented Reality
BA | Behavioral Intervention
CBT | Cognitive Behavioral Therapy
CCBT | Computer-Based Cognitive Behavioral Therapy
CD | Cognitive Distortions
CNN | Convolutional Neural Network
CR | Cognitive Restructuring
CTRS | Cognitive Therapy Rating Scale
DLPFC | Dorsolateral Prefrontal Cortex
DoT | Diagnosis of Thought
ECA | Embodied Conversational Agent
ET | Exposure Therapy
GAD | Generalized Anxiety Disorder
GD | Gambling Disorder
GPT | Generative Pretrained Transformer
HDRS | Hamilton Depression Rating Scale
ICBT | Internet-Based Cognitive Behavioral Therapy
IECBT | Internet-enabled CBT
LiCBT | Low-Intensity Cognitive Behavioral Therapy
LLM | Large Language Model
MHA | Mental Health America
MUBS | Mobile-based Behavioral Activation System
NLP | Natural Language Processing
OCD | Obsessive-Compulsive Disorder
PD | Panic disorder
PDT | Psychodynamic Therapy
PTM | Pre-trained Language Model
PTSD | Post-Traumatic Stress Disorder
Q&A | Question and Answering
ReHo | Regional Homogeneity
RET | Rational Emotive Therapy
SAD | Social Anxiety Disorder
THI scores | Tinnitus Handicap Inventory scores
VR | Virtual Reality
VRET | Virtual Reality Exposure Therapy
XAI | Explainable Artificial Intelligence

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