* These authors contributed to the work equllly and should be regarded as co-first authors. Further author information: (Send correspondence to<EMAIL_ADDRESS>
# Towards a Psychological Generalist AI: A Survey of Current Applications of
Large Language Models and Future Prospects

Tianyu He* Center for Wise Information Technology of Mental Health Nursing
Research, School of Nursing, Wuhan University, Wuhan, China  Guanghui Fu*
Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS,
Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France  Yijing
Yu  Center for Wise Information Technology of Mental Health Nursing Research,
School of Nursing, Wuhan University, Wuhan, China  Fan Wang  Center for Wise
Information Technology of Mental Health Nursing Research, School of Nursing,
Wuhan University, Wuhan, China  Jianqiang Li  School of Software Engineering,
Beijing University of Technology, Beijing, China  Qing Zhao  School of
Software Engineering, Beijing University of Technology, Beijing, China
Changwei Song  School of Software Engineering, Beijing University of
Technology, Beijing, China  Hongzhi Qi  School of Software Engineering,
Beijing University of Technology, Beijing, China  Dan Luo  Center for Wise
Information Technology of Mental Health Nursing Research, School of Nursing,
Wuhan University, Wuhan, China  Huijing Zou  Center for Wise Information
Technology of Mental Health Nursing Research, School of Nursing, Wuhan
University, Wuhan, China  ✉ Bing Xiang Yang  Center for Wise Information
Technology of Mental Health Nursing Research, School of Nursing, Wuhan
University, Wuhan, China

###### Abstract

The complexity of psychological principles underscore a significant societal
challenge, given the vast social implications of psychological problems.
Bridging the gap between understanding these principles and their actual
clinical and real-world applications demands rigorous exploration and adept
implementation. In recent times, the swift advancement of highly adaptive and
reusable artificial intelligence (AI) models has emerged as a promising way to
unlock unprecedented capabilities in the realm of psychology. This paper
emphasizes the importance of performance validation for these large-scale AI
models, emphasizing the need to offer a comprehensive assessment of their
verification from diverse perspectives. Moreover, we review the cutting-edge
advancements and practical implementations of these expansive models in
psychology, highlighting pivotal work spanning areas such as social media
analytics, clinical nursing insights, vigilant community monitoring, and the
nuanced exploration of psychological theories. Based on our review, we project
an acceleration in the progress of psychological fields, driven by these
large-scale AI models. These future generalist AI models harbor the potential
to substantially curtail labor costs and alleviate social stress. However,
this forward momentum will not be without its set of challenges, especially
when considering the paradigm changes and upgrades required for medical
instrumentation and related applications.

###### keywords:

Generalist AI, Mental health, Large language model, Psychology, Foundation
model

##  1 Introduction

Mental health conditions are prevalent across the globe. However, many
societies and their corresponding health and social systems overlook mental
health, failing to offer the requisite care and support. Consequently,
countless individuals worldwide endure these challenges silently, face human
rights violations, or encounter adversities in their daily lives [1].
Psychological well-being and understanding are of paramount importance in
today’s rapidly evolving world. Across the globe, individuals grapple daily
with the dynamic nature of their psychological states, influenced by both
external and internal factors. Whether it’s individuals expressing their
sentiments on social media [2], students and teachers confronting mental
health challenges in academic settings [3], or the overarching demand for
professional psychological counselors [4], the implications are evident—mental
health matters. Prompt interventions, especially in critical situations such
as potential suicides, can save lives [5, 6]. Moreover, timely support and
guidance can ameliorate numerous other psychological challenges, ensuring
healthier communities [7].

The realm of artificial intelligence (AI) has witnessed tremendous
advancements in recent years, offering promising tools to address these
pressing psychological concerns[8, 9]. Deep learning techniques, backed by
expert annotations, have been heralded for their impressive performance in
various applications [10, 11]. Yet, the heavy reliance on extensive
annotations introduces significant costs, which often becomes a barrier for
many institutions and researchers [12].

Enter the era of large language models (LLMs), epitomized by models like GPT
[13, 14]. These models, pre-trained on vast datasets, leverage intricate
neural networks and techniques from reinforcement and supervised learning
[15]. Their ability to interface with humans through prompts allows them to
address a multitude of tasks, thereby circumventing the need for extensive
expert-labeled data [16]. Not only does this foster a spirit of
experimentation in psychological research, but the inherent conversational
proficiency of these models positions them as empathetic listeners and virtual
companions [17].

While the psychological domain is undoubtedly poised for a paradigm shift due
to these AI advancements, the horizon holds even more promise [18, 19]. The
emergence of multi-modal generalist AI models—capable of integrating data from
various modalities—foreshadows an era where AI can intuitively grasp and
execute diverse human tasks[20, 21]. These models can discern subtle
psychological shifts, hypothesize about uncharted psychological territories
leveraging multidisciplinary knowledge, and even pioneer novel paradigms under
human guidance.

Generalist AI in psychology promises a future where machines can not only
model but also predict forthcoming psychological dynamics. While the journey
to a holistic generalist AI remains an ongoing endeavor, current strides in
the field are illuminating the path [22]. In this paper, we provide a
comprehensive review of the latest developments in the utilization of LLMs for
psychological research. Through a systematic exploration of application
domains, target populations, and the extraction of psychological constructs,
we envision and articulate the transformative potential of generalist AI in
the realm of psychology.

The structure of this paper is outlined as follows. Initially, in Section 2,
we introduce the concept of generalist AI, providing a foundational
understanding for readers. This is followed by Section 3, which delves into
the critical aspect of model validation, a fundamental prerequisite for
practical application. Next, in Section 4, we explore the advancements of
Large Language Models (LLMs), particularly the GPT series, across various
domains. Section 5 then summarizes the current perspectives and a range of
research papers discussed in this review. In Section 6, we present potential
application areas of generalist AI, considering factors such as the
psychotherapy process, scenarios, age groups, and specific psychological
diseases. Finally, Section 7 highlights several key aspects that warrant
focused attention for the future integration of AI in psychology, culminating
in the conclusion in Section 8.

##  2 The potential of generalist AI models in psychology

Generalist Psychology Artificial Intelligence (GPAI) holds promise for
tackling a broader range of intricate tasks across multiple scenarios and
demographic groups, surpassing the capabilities of current AI models. This
innovation paves the way for specific tasks to be executed with minimal or
even no labeling requirements. GPAI is designed to cater to the demands of
diverse fields, ensuring a flexible and responsive user interaction. It
encompasses several key functionalities. First and foremost, under ethical
constraints, GPAI emphasizes appropriate monitoring: it seeks to diligently
track user mental health across diverse situations, always prioritizing
privacy and ensuring user transparency. Second, flexible interaction of data
modalities: it exhibits the capability to interact with and integrate multiple
data modalities. Even when users analyze complex data sets, GPAI can generate
outputs that seamlessly combine these varied modalities. Third, advanced
reasoning and prediction: GPAI leverages psychological and medical knowledge
to conduct sophisticated reasoning. It can anticipate future psychological
states and unearth novel psychological insights, enhancing understanding and
potential interventions. Overall, GPAI aspires to be a multifaceted tool,
adept at adapting to individual needs while offering advanced analytical
capabilities in the realm of psychology. For a basic view of all prospective
scenarios, refer to Figure 1.

Figure 1: Future applications of GPAI. (a) GPAI monitors patient health in the
ward (b) GPAI assists doctors in psychological consultation (c) GPAI assists
doctors in analyzing and diagnosing psychological data (d) GPAI supports
people with negative emotions in social media.

##### Ethical psychological monitoring

Given the fluctuating nature of human psychology and the intricate landscape
of mental states, consistent real-time expert companionship and surveillance
are challenging. Diseases like depression can manifest abruptly, leading,
tragically, to suicides that might be prevented [23]. For instance, many
depression sufferers, either on social media or in their daily lives, may not
actively seek help [24]. However, they might reach out to a suicide prevent
line [25] or express suicidal intentions online [26] under some special topics
as a cry for assistance. An individual who takes their own life has typically
attempted suicide several times previously [27]. Herein lies the potential for
generalist AI: introducing universally accessible suicide hotlines and
ethically monitoring online platforms for potential risks. This can bridge the
gap in expert availability and reduce suicide incidences. In psychiatric
rehabilitation centers, GPAI can be employed to detect anomalies, including
potential suicide attempts or other emergencies [28]. In such applications,
maintaining patient privacy remains of utmost importance [29].

##### Multimodal human-AI collaboration

GPAI should adeptly manage multi-modal data inputs provided by users and
reciprocate with feedback in the desired format. The psychological domain
commonly deals with textual medical records, visual data from social
platforms, audio from interviews, tabulated psychological assessments, and
psychoanalytic imaging data. Catering to diverse user needs might entail
multiple data modality interactions. For instance, monitoring social media for
potential risks requires GPAI to scrutinize both images and text, discerning
varying suicide risks [30, 31], and subsequently facilitating interventions or
referrals at the optimal moment. In aiding doctors during psychological
assessments or therapeutic sessions, GPAI should process real-time dialogues
between patients and clinicians, offering intervention tactics and
psychological profiles to enable more tailored treatments [32]. Hence, GPAI’s
adaptability in handling multiple data modalities is indispensable [33, 21].

##### Enhanced reasoning and predictive capabilities

Equipped with a wealth of knowledge spanning domains like psychology and
medicine, GPAI is poised to complement human expertise, deducing novel mental
states and disease attributes based on established knowledge. For instance,
the emergence of COVID-19 has impacted diverse demographic groups’
psychological well-being. In these novel scenarios, GPAI can identify
predominant psychological challenges faced by distinct groups—be it healthcare
professionals [34, 35], adolescents [36], or the general public [37]—and
proffer appropriate recommendations and interventions [38]. GPAI holds promise
in assisting experts in deciphering and modeling mental illness progression,
such as forecasting potential disease trajectories using data on a patient’s
mood, familial, school or societal support structures, medication, and more,
enriching our comprehension of these conditions. GPAI’s predictive prowess,
like estimating future suicide rates based on hotline interactions, can
significantly aid experts in more precise interventions and suicide prevention
[39].

##  3 Model evaluation

Model evaluation is essential for understanding the capabilities and
limitations of LLMs. It encompasses various evaluation platforms such as
Chinese Language Models EVAluation Platform (CLEVA) [40], and datasets like
CHBias [41], enabling potential users to grasp the models’ scope of competence
[42, 43, 44, 45, 46]. This section focuses on two primary aspects: task-
specific evaluations in Psychology, particularly in assessing models on
datasets related to cognitive biases and suicide [47]; and the evaluation of
models’ psychological characteristics in specific scenarios, such as their
emotional intelligence [48]. This research enhances our understanding of LLMs’
specific performance, crucial for studies simulating human subjects. Further
details are discussed in the subsequent sections. The specifics of LLM
evaluation are extensively covered in the literature [49].

###  3.1 Task-specific evaluations in psychology

The performance of LLMs in the field of psychology can be assessed through
various task-based validation methods with specific criteria for evaluation.
These tasks could include emotion recognition or emotional-awareness, and
mental health classification such as depression detection or suicidal risk
detection.

Recently, researchers indicated that the existing uses of AI for the field of
mental health are limited in their capabilities in emotional-awareness [50].
Emotional-awareness was assessed using the level of emotional awareness scale
(LEAS) in twenty different scenarios, comparing GPT-3.5’s performance with
general population norms [51, 52]. A subsequent evaluation assessed GPT-3.5’s
progress in emotional-awareness and involved independent licensed
psychologists evaluating the contextual appropriateness of GPT-3.5’s
emotional-awareness responses, reaching almost the maximum LEAS score (Z
score=4.26), with an exceptional accuracy level of 9.7/10. Wake N, et al. [53]
investigated GPT-3.5’s ability in emotion recognition from text, essential for
applications like interactive chatbots and mental health analysis. While prior
studies demonstrated GPT-3.5’s proficiency in positive or negative sentiment
analysis [54, 55], its nuanced emotion recognition remained unexplored.
Experiments revealed reproducible performance with improvements through fine-
tuning. However, performance inconsistencies across emotion labels and
datasets suggested inherent instability and potential bias. The choice of
dataset and emotion labels significantly influenced GPT-3.5’s emotion
recognition. The study emphasizes the importance of dataset selection and
fine-tuning, laying the foundation for improved emotion analysis integration
in GPT-3.5 applications. Furthermore, some researches have explored LLMs’
capacities of identifying mental health issues, which included stress [56],
depression [56], cognitive distortions [47], and suicidal risk [47, 56, 57].
Using supervised learning as a baseline, a study experimented two pivotal
tasks: suicidal risk and cognitive distortion identification on Chinese social
media platforms through three distinct strategies: zero-shot, few-shot prompt,
and fine-tuning [47]. The findings revealed a noticeable performance gap
between LLMs and traditional supervised learning approaches, primarily due to
the models’ inability to fully grasp subtle categories. Notably, while GPT-4
outperformed other versions in multiple scenarios, GPT-3.5 exhibited
significant enhancement in suicide risk classification after fine-tuning.
Lamichhane’s study evaluate the performance of LLM-based ChatGPT in various
mental health classification tasks, reporting F1 scores of 0.73 for stress
detection, 0.86 for depression detection, and 0.37 for suicidality detection
[56]. Levkovich I and Elyoseph Z [57] focuses on ChatGPT’s ability in suicide
risk assessment, particularly analyzing two discernible factors: perceived
burdensomeness and thwarted belongingness. Their study includes comparisons
between assessments conducted by GPT-3.5, GPT-4 and mental health
professionals. The findings suggest that GPT-4’s assessments of suicide
attempt likelihood are in close agreement with those made by mental health
professionals. This reveals its accuracy in identifying suicidal ideation.
However, its tendency to overestimate psychological pain (psychache) points to
the necessity for additional research. In comparison, GPT-3.5 often
underestimates suicide risk, highlighting concerns about its precision,
particularly in critical cases. These results underscore the potential of LLMs
in mental health settings, laying the groundwork for their continued
improvement and wider use.

###  3.2 LLM performance in psychological contexts

Recent studies have started to assess the exploratory abilities of LLMs in
psychological context. While this kind of evaluation often lack quantfiable
criteria, but can let people understand how it performs. This exploration is
particularly crucial for exploring situations unknown to humans or unclear to
define. Ullman investigated the performance of LLMs in reasoning tasks related
to intuitive psychology [58].This study highlists that several recent tasks
and benchmarks assessing the reasoning ability of LLMs have primarily focused
on belief attribution in Theory-of-Mind tasks [58]. He et al. [59] have
initiated research on using LLM as the driving core of social bots. They
employed tasks such as profile completion, social behavior decision-making,
and social content generation to explore models’ capabilities. Furthermore,
benchmarks in the domains of planning and explanation generation were
introduced to test the limit of LLMs’ abilities in reference [60]. To enhance
problem solving and human-like reasoning in LLMs, a hybrid Parse-and-Solve
model was developed to integrate distributional LLMs with a structured
symbolic reasoning module. Experiments have been conducted to testify LLMs’
communication and emotional abilities. A new psychometric assessment focusing
on emotion understanding was developed to assess Emotional Intelligence, which
includes emotion recognition, interpretation, and understanding, all crucial
for effective communication and social interactions [48]. This test is
applicable to both humans and LLMs and involves interpreting complex emotions
in realistic scenarios. Additionally, an opinion network dynamics model that
incorporates LLMs’ opinions, individuals’ cognitive acceptability, and usage
strategies, was created to explore its capacity in effective demand-oriented
opinion network interventions [61].

##  4 Progress and Applications of LLM in Psychology

In this section, we introduce the applications of LLM in three scenarios:
digital media platforms, clinical nursing care practice and community. We also
discuss its progress in exploring psychological theories and data generation.

###  4.1 Digital and social media platforms

####  4.1.1 Sentiment detection and analysis

The detection of psychological constructs, mental state and suicide risk
through social media content has become increasingly important for early
diagnosis and prevention of adverse consequences related to user’s
psychological states [62, 63]. Qin et al. [64] developed a system that
provides precise diagnoses and evidence based insights. This system analyze
user’s mental state through social media conversations. This system offers
tailored recommendataions to promote well-being and integrates advanced
techniques with expert criteria to enable informed decisions. The experiments
demonstrate that the system achieves the best performance across various
settings, including full data setting, few-shot setting, zero-shot setting,
independent-identical-distribution (IID) setting, and out-of-distribution
(OOD) setting. Notably, under the IID setting, the system consistently attains
optimal performance across various data configurations and metrics. Rathje et
al. [65] explored GPT-3.5’s capability for automated psychological text
analysis in various languages, finding its accuracy in detecting psychological
constructs like sentiment, discrete emotions, and offensiveness to be
satisfactory and superior to many current automated text analysis methods.
LLMs like GPT-3.5 are valuable for screening suicide risk on social media. Qi
et al. [47] introduced a comprehensive benchmark for evaluating LLMs on
cognitive distortions or suicidal risk. This benchmark assesses the LLM’s
abilities in detecting these concerns using social media data. As task
complexity increases, the performance of LLMs tends to decline. While fine-
tuning LLMs can enhance their effectiveness in relatively straightforward task
like suicide classification, this approach falls short in more complex
cognitive distortion task. This indicates a need for further research into
advanced fine-tuning mechanisms for LLMs.

####  4.1.2 Intelligent Q&A chatbots

Q&A Chatbots powered by LLMs have increasingly been utilized in mental health
support [66]. Cabrera et al. [67] recommended the use of LLM based Q&A
chatbots, specifically tailored for mental health to complement the care
provided by human professionals. A qualitative analysis of 120 posts,
including 2917 user comments showed that the Replika app offers on-demand,
non-judgmental support, boosts user confidence, and aiding self-discovery
[66]. In a patient care context, Chen et al. [68] demonstrated the feasibility
of utilizing a ChatGPT-driven chatbot in mental health scenarios. The chatbot,
capable of simulating experts or patients, showed a high level of similarity
in its interactions. Cai et al. [69] developed PaniniQA, a system designed to
improve patient understanding of their discharge instructions through
interactive question answering. They claimed that the sytem is capable of
identifying important clinical content and effectively generating patient-
specific educational questions. Despite the benefits, there are still some
challenges in LLM based Q&A Chatbots. Ma et al. [66] identified issues such as
difficulty in filtering harmful content, maintaining consistent communication,
managing the retention of new information, and addressing user overdependence.
Fu et al. [32] employed a vector database to detect content produced by LLMs
in online psychological counseling support system, thereby preventing the
generation of harmful opinions. Additionally, there is a concern that social
stigma associated with using these chatbots could further isolate users [66].
Several studies strongly assert that future researchers and designers must
conduct a comprehensive evaluation of LLMs in mental well-being support. This
is essential to ensure the responsible use and proper regulation at both
national and international levels [66, 67].

####  4.1.3 Online psychological support tools

Several studies have demonstrated that LLMs like GPT-3.5 or GPT-4 can
effectively assist human in online psychological counseling and answering
professional questions with increased empathy and acceptable feasibility [70,
32, 68].

Ayers et al. [70] conducted experiments to compare the response of physicians
and chatbots to patient questions on a public social media platform. The
results showed that responses generated by chatbots were preferred and rated
significantly higher in terms of both quality and empathy. This finding
indicates that AI assistants could be valuable in assisting physicians in
formulating responses to patient queries, potentially enhancing the
effectiveness and emotional resonance of online mental consultations. In light
of the increasing negative emotions especially concerning suicidal intentions
expressed on social media, there is a critical need of trained psychological
counselors [32]. However, the shortage of trained professional counselors has
prompted the necessity to involve non-professionals or volunteers in providing
online psychological support. Fu et al. [32] proposed a novel model based on
LLMs to empower non-professional counselors to offer effective psychological
interventions online. An evaluation involving ten professional counselors
demonstrated the system’s efficacy in analyzing patients’ issues and offering
professional-level recommendations. The utilization of such models can
effectively bridge the gap between the demand for psychological counseling and
the limited availability of professional resources. Cacers et al. [71]
introduced Well-Mind ChatGPT (WMGPT), a system offering 24/7 mental health
counseling services which utilized a fine-tuned ChatGPT model. This system
presents a promising solution by offering accessible, personalized, and timely
mental health support. It effectively overcomes the limitations of traditional
counseling methods and enhancing overall well-being. Lai et al. [72] proposed
Psy-LLM framework, an AI system employs LLMs to enhance question-answering
capabilities in online psychological consultation. The effectiveness of this
system was evaluated using metrics such as perplexity and human assessments,
demonstrating the capability in generating coherent and relevant answers.
These studies emphasized the potential of LLMs to enhance mental health
support in online psychological consultation.

###  4.2 Clinical and care settings

####  4.2.1 Mental interview, psychological counselor assistance

Currently, there is a contradiction between the increasing number of
individuals experiencing emotional distress and the limited number of
professional counselors, coupled with lengthy training periods and high costs.
LLMs can play a significant role in assisting psychological counseling
scenarios. Eshghie et al. [73] suggested using ChatGPT as an adjunct in
psychotherapy, where it could act as a tool for collecting patient
information, provide companionship to patients between therapy sessions, and
organize information for therapists to streamline treatment processes. GPT-3.5
can participate in positive conversations and provide validation or coping
strategies, which can help therapists engage in the counselling. Chen S et al.
[68] utilized ChatGPT to simulate psychiatrist and patient, collaborating with
psychiatrists to develop the dialogue system aligned with real-world
scenarios. Furthermore, LLMs have demonstrated exceptional performance in
online counseling. Research indicates that LLMs can accurately analyze patient
issues and provide professional-level recommendations, thereby augmenting
support for non-professional counselors [32].

####  4.2.2 Wearable devices and behavior monitoring

LLMs excel are adept at grasping complex concepts, yet real-world health
applications require integration with numerical data. Liu et al. [74] showed
that, LLMs can effectively analyze physiological and behavioral time-series
data with minimal tuning. This capability extends to tasks such as cardiac
signal analysis, physical activity recognition, metabolic calculation, and
stress estimation using data from wearable and medical sensors.

###  4.3 Community-based psychological screening tools

####  4.3.1 Focused on children and adolescents

LLMs offer notable advantages for mental health support among children and
adolescents. Specifically, GPTs show notable potential in offering initial
assistance to young adults facing mental health challenges [75]. Dosovitsky G
and Bunge E [76] explored the use of chatbots for mental health among
adolescents, an area that has been less studied in this context compared to
adults. Twenty-three participants aged 13–18 tested a chatbot designed to
educate about depression, teach behavioral activation, and helping in
addressing negative thoughts. The study revealed moderate level of
satisfaction among participants with over half providing positive feedback.
This suggests that mental health chatbots are generally well-received by
adolescents and can be a valuable tool in addressing their mental health
needs. While recognizing the benefits, it is crucial to carefully address the
limitations of LLMs which necessitate more thorough investigation [77, 78].
Aditama et al. [77] identified that the LLM’s limitation including epistemic
injustice like interpretive value and an ethically flawed affective value
which may lead to a diminished effectiveness. More seriously, relying solely
on LLMs for mental health support without professional guidance might overlook
essential assessments, monitoring, and treatment, potentially increasing the
risk of suicide attempt [78]. LLM applications for children should prioritize
safety, particularly within an ethical framework.

####  4.3.2 Educational settings and students

Researchers are increasingly exploring the use of LLMs in mental health
education and as tools for student social engagement. As an interactive
chatbot powered by GPT-3.5 can be effective in educating students about
psychological impacts of sexual violence [79]. Its interactive and accessible
nature makes it a valuable tool for disseminating information, aiding in the
prevention of sexual violence, and supporting treatment efforts. Moreover,
integrating GPT-3.5 into educational curricula can raise awareness and assist
affected students. LLMs may also serve as alternatives for students’
interpersonal relationships. Maples et al. [80] conducted a survey with over
1000 student users of the intelligent social agent, Replika. They found that
these participants, despite being lonelier than typical students, perceived
high social support from Replika. The chatbot, perceived by users as a friend,
therapist, or intellectual mirror, could play a significant role in reducing
feelings of loneliness and suicidal ideation among the students.

###  4.4 Advancing psychological theories

####  4.4.1 Simulating social networks

The use of LLMs can significantly contribute to the study of simulated social
networks, providing researchers with valuable tools to observe and explore
various psychological theories. He et al. [59] conducted a study exploring the
behavior of LLMs within a simulated social network. They designed a Twitter-
like platform called Chirper that populated only by LLMs. Their analysis
revealed evidence of self-organized homophily in the system, demonstrating
patterns that resemble human social tendencies. Although the content generated
by chatbots is generally more generic compared by human, this observation
highlights the potential for developing LLM-driven Agent-Based Models. Such
models could enhance the comprehensive understanding of human social dynamics
in social scientific research. Gao et al. [81] leveraged the abilities of LLMs
to develop the Social Network Simulation System. This system, employing agent-
based simulation techniques, effectively replicated authentic human behavior,
capturing nuances in emotions, attitudes, and interactions. Park et al. [82]
introduced a novel prototyping method called social simulacra. This technique
employs LLMs to simulate the interactions of diverse community members. It is
based on a designer’s specifications about the community’s design goals,
rules, and member personas. The evaluation of both studies have shown
promising accuracy, marking a signifying step forward in the domain of LLM-
based social network simulation.

Besides, based on certain theories, LLMs have been evaluated for specific
capabilities. A recent study investigated the social intelligence and Theory
of Mind (ToM) capabilities of GPT-3. This involves the ability to understand
various mental states, intentions, and reactions, which is crucial for
effectively navigating everyday social interactions [83]. The research
assessed GPT-3’s performance on SocialIQa and ToMi tasks, focusing on its
comprehension of social interactions and ability to deduce mental states. The
findings revealed that GPT-3’s accuracy in these areas is below human levels.

####  4.4.2 Modeling individual agents

Another emerging application of LLMs is in simulating specific populations,
aiding research focused on these demographics.Various studies have evaluated
the accuracy of these simulations. The methods used include conducting Turing
Experiments [84], comparing the simulated responses with those from human
samples [85], and recruiting real psychiatrists or patients in diagnostic
conversations with the chatbots [68]. These approaches help in assessing the
realism and relevance of LLM simulations in representing actual populations.
Chen et al. [68] and Aher et al. [84] demonstrated the potential of LLMs in
accurately simulating diverse human interactions for various research and
practical applications.

####  4.4.3 Simulating environments and contexts

Currently, interpersonal interactions such as transactions, role reversal, as
well as interactive scenarios like classrooms and communities simulated by
LLMs are emerging.

LLMs extend beyond simulating interactions between two individuals; they are
equally adept at simulating interactions among multiple individuals in
settings such as community or educational environments. Park et al. [86]
introduced generative agents, computational entities that mimic genuine human
behavior. These agents are capable of possessing life experiences, making
autonomous decisions, and engaging in realistic interactions. They can form
opinions and participate in conversations. When implemented in an interactive
sandbox environment, these agents allow user interaction through natural
language. This represents a significant advancement in the creation of
believable simulations of human behavior, achieved by integrating LLMs with
interactive computational agents. Shi et al. [87] successfully replicated
realistic human interactions in various scenarios, particularly focus on
classroom environment. They utilized a framework named Configurable General
Multi-Agent Interaction, based on LLMs. The experiments conducted with this
framework showcased its capability to closely mimic real classroom dynamics,
including teaching methods, curriculum, and student performance. Furthermore,
LLMs have the capability to simulate environments, assessing the agents’
proficiency in mimicking diverse roles presented by another agent. Kovavc et
al. [88] introduced a SocialAI school driven by LLMs, a tool designed for
customizable, procedurally generated environments to facilitate experiments
related to the socio-cognitive abilities of LLMs. This platform enabled the
examination of model capabilities such as role-reversal. The findings indicate
a promising research direction in guiding an agent’s attention towards
understanding and adopting the partner’s role.

####  4.4.4 Applications and innovations in psychological theory

As technologies progress, the application of LLMs in field such as
understanding mentalizing abilities and personality structures is growing,
particularly focusing on the exploration of human emotions on a theoretical
bases. Despite the fundamental differences between machine intelligence and
human emotions, LLMs have demonstrated potential in adopting various
personalities [89]. Hadar-Shoval et al. [90] explored the ability of GPT-3.5
to generate mentalizing-like responses tailored to specific personality
structures, such as Borderline Personality Disorder and Schizoid Personality
Disorder. While the responses aligned with clinical knowledge, concerns about
potential stigmas and biases related to mental diagnoses emphasized the
necessity for responsible development in chatbot-based mental health
interventions. Binz and Schulz [91] transformed LLMs into cognitive models by
fine-tuning with psychological experiment data. This resulted in accurately
representing human behavior and outperforming traditional cognitive models in
decision-making domains. Ullman et al. [58] explored the intuitive psychology
in LLMs for belief attribution in theory-of-mind tasks revealing both
successes and failures. The research emphasized skepticism about zero-
hypothesis in model evaluation and highlighted the importance of prioritizing
exceptional failure cases over average success rates. In conclusion, LLMs’
adaptability, even in unseen tasks, suggested their potential as generalist
models, paving new pathways in cognitive psychology, behavioral sciences and
intuitive psychology [91, 58].

###  4.5 Data Generation techniques

Language and cognition research extensively relies on large-scale psycho-
linguistic datasets, which include judgments on lexical properties such as
concreteness and age of acquisition [92]. These datasets are essential for
standardizing experimental stimuli, revealing empirical relationships within
lexicons, training deep learning models, and serving a range of research
objectives. However, the economic and time constraints associated with data
collection make acquiring such large scale dataset increasingly challenging.
Trott [92] investigated the potential of utilizing advancements in LLMs,
particularly GPT-4, to facilitate the creation of extensive psycho-linguistic
datasets in English. This research involved a comparison between semantic
judgments generated by LLMs and those made by human, revealing positive
correlations. In some instances, LLM-generated judgements even exceeded human
inter-annotator agreement. The study also examined the systematic differences
between norms generated by LLMs and humans, providing further insights into
the performance of LLMs in this context.

##  5 Synopsis of the viewpoints in literature

In this section, we provide a summary of the viewpoints expressed in current
reviews and opinion papers regarding the application of LLMs in the medical
field and associated domains.

###  5.1 Potential application

In summary, these references [93, 94, 95, 18, 96, 19] highlight the wide-
ranging applications of LLMs in healthcare, including tasks like medical
question answering, generation of comprehensive assessments, clinical
conversations, and medical research. The works of Demszky et al. [18] and
Zhong et al. [96] discuss LLMs’ potential in measurement and experimentation,
particularly in psychiatric research within psychology subfields. Zhong et al.
[96] further suggest that LLMs can enhance diagnostic accuracy and
personalized treatment in neuropsychiatry. Thirunavukarasu et al. [19]
underscore the necessity of addressing ethical and technical challenges in LLM
deployment. Moor et al. [21] emphasize the need for generalist medical
artificial intelligence (GMAI) to adapt flexibly to new tasks and data
patterns. Cabrera et al. [67] focus on LLMs’ potential in improving mental
health counseling, patient care, diagnostic accuracy, and treatment planning.
Mao et al. [97] observe that LLMs’ task performance in specific fields still
falls short of expert models. Lastly, Kaddour et al. [98] stresses the
importance of LLM-based toxicity detection tools in mitigating harmful content
output.

###  5.2 Bias and fairness issues

Overall, the references [93, 18, 21, 67, 97, 15, 98, 95] underscores fairness
as a critical issue in the utilization of LLM and natural language processing
(NLP), highlighting potential biases that may lead to inconsistencies, unequal
treatment, and discrimination. These biases in LLMs can manifest as prejudiced
sentiments based on gender, race, religion, or political beliefs, potentially
affecting healthcare outcomes, such as the underdiagnosis in marginalized
communities. Further, the references [99, 19, 94, 98] notes that biases in
LLMs may arise from perspective biases in the training data sourced from
internet text corpora, thus raising ethical concerns. Keeling et al. [99]
suggest that ethical nudges, though not a complete solution, can mitigate bias
when combined with appropriate evaluative measures. They emphasize the
importance of inclusive and open discussions on who fine-tunes these models
and the standards applied. Demszky et al. [18] argue that while addressing
bias is crucial, examining it can also provide insights into the dominant
culture of the training data. Moor et al. [21] recommend ongoing audit
monitoring and prize competitions to encourage scrutiny and bias
identification. Additionally, references [97, 15] highlights the challenges
LLMs’ generative capabilities pose to fact evaluation. Zhao et al. [15] raise
concerns regarding copyright and personal information leakage. Arora et al.
[95] notes the impact of generated inflammatory content on mental health,
while Zhong et al. [96] point out the need for reliability, accuracy, and
transparency in LLMs, particularly in their application to mental illness,
emphasizing the focus on ethical issues.

###  5.3 Moderation approach

He et al. [93] highlight a range of strategies for mitigating bias in LLMs,
including the integration of specific anti-discrimination components and
modules, alongside prompt engineering and instruction fine-tuning to promote
fairness. Keeling et al. [99] argue for the re-evaluation of biases in
generalist models, which may differ from previously held assumptions about
systematic biases. Demszky et al. [18] emphasize the need for human expert
oversight in LLM interactions, particularly when dealing with vulnerable
groups. Thirunavukarasu et al. [19] note that adversarial prompts can exploit
vulnerabilities in LLMs, leading to toxic outputs. These references [21, 95]
stresses that GMAI models, after accessing extensive patient data, may face
privacy issues. It’s essential to minimize the risk of sensitive data exposure
and to review potential attacks on the model effectively. Cabrera et al. [67]
assert that interdisciplinary research involving diverse talents is crucial to
avoid bias. Mao et al. [97] suggest using Reinforcement Learning with Human
Feedback (RLHF) to reduce bias and toxic responses in LLMs, while cautioning
against system gaming and aggressive reward loops that could mislead the
training process. Zhao et al. [15] state that adversarial methods can
effectively explore LLMs to prevent harmful outputs. Singh et al. [100]
commend the American Psychiatric Association (APA) for creating a Digital
Psychiatry Task Force and introducing an application evaluation model to
assess the efficacy, safety, and tolerability of digital applications. The
references [96, 100] underlines the importance of standardization, monitoring,
and guidance to address potential safety concerns. Zhong et al. [96] highlight
the need for transparency and accountability mechanisms in these technologies.

###  5.4 Privacy Issues

The references [93, 99, 18, 19, 21, 67, 15] underscores the significance of
privacy in healthcare applications, emphasizing the need for careful
evaluation and regulation. He et al. [93] suggest that Federated learning
models hold promise for protecting patient privacy while enabling large-scale
evaluation of LLMs. Arora et al. [95] acknowledge emerging solutions like
federated learning and synthetic data but note uncertainties regarding their
suitability for LLMs. Demszky et al. [18] and Singh et al. [100] argue that
clear data privacy and protection processes are crucial for ethical practices
in psychological research. Moor et al. [21] highlight the risks to patient
privacy posed by GMAI models, which acquire rich patient characteristics. The
potential exposure of sensitive patient data in training datasets and the risk
of sensitive information being revealed through prompt attacks are major
concerns. They advocate for de-identification and restricting information
collection to mitigate these privacy risks. Conversely, Thirunavukarasu et al.
[19] point out a conflict: while privacy protection is critical, limiting
patient identifiable data in model prompts could hamper deployment
opportunities. This situation presents a potential conflict between personal
identity and legal rights, underscoring the complexity of balancing privacy
concerns with the effectiveness of LLMs in healthcare.

###  5.5 Monitoring and evaluation

The references [93, 99, 19, 21, 67, 95, 49, 98, 96] emphasizes the necessity
of continuous monitoring and dynamic evaluation of LLMs in the medical field,
focusing on aspects such as accuracy, fairness, transparency, and adherence to
ethical standards. Keeling et al. [99] highlight the need for the development
and integration of bias mitigation techniques into regulatory policies,
suggesting that evaluations should consider not just performance but also
clinical semantic biases. Demszky et al. [18] advocate for clear processes to
quantify and reduce bias, emphasizing the importance of investments in
datasets, the standardization of performance benchmarks, and shared
infrastructure to address ethical concerns. Thirunavukarasu et al. [19] note
the challenges in assessing LLM performance on clinical tasks, particularly
regarding their impact on mortality and morbidity. They suggest that
randomized controlled trials are essential, but finding appropriate benchmarks
and endpoints is complex. Cabrera et al. [67] call for the monitoring and
evaluation of chatbots’ impact on mental health outcomes to ensure the
quality, safety, and effectiveness of these interventions. The references [97,
94] points out that current evaluations, heavily reliant on prompt engineering
and standard datasets, may be unreliable. It also raises the question of how
to fairly compare the performance of expert systems and LLMs in professional
fields. Zhao et al. [15] state that while manual evaluation can accurately
measure LLM performance in real-life scenarios, it is time-consuming and not
scalable. They suggest an efficient and scalable model-based evaluation. Chang
et al. [49] advocate for a trustworthy and unified evaluation system to
provide comprehensive guidance and analysis for future research. Finally, the
references [98, 96] underscores the importance of detection tools, such as
those for identifying toxicity and deceptive outputs, in the ongoing
development and application of LLMs.

##  6 Towards generalist AI in psychology

In this section, we explore the diverse potential applications of generalist
AI in the field of psychology. The various application directions are
illustrated in Figure 2. Additionally, Figure 3 employs GPT-3.5 as an
exemplary model to showcase the latest developments in AI. While GPT-3.5 does
not exemplify generalist AI, it effectively highlights the significant strides
achieved in the field to date. The examples depicted in the figure correspond
to the content discussed in the subsequent sections.

Figure 2: Applications of GPAI in various aspects of psychology. Figure 3:
Experimental psychological scenarios utilizing the large language model
(GPT-3.5) for testing purposes.

###  6.1 Generalist AI in psychotherapy process

##### Psychological assessment

Generalist AI models could revolutionize the way psychological assessments are
conducted. By analyzing a plethora of patient data, from spoken words to non-
verbal cues, AI can assist clinicians in painting a holistic picture of a
patient’s psychological state. Furthermore, through continuous learning, these
AI models can identify patterns and nuances often missed by the human eye,
ensuring more comprehensive assessments.

##### Risk prediction

Predictive analytics, powered by AI, can be instrumental in forecasting
potential psychological risks. By parsing through historical data, genetic
information, and current behavioral patterns, AI can predict susceptibility to
certain disorders or the likelihood of symptom exacerbation, enabling timely
interventions.

##### Diagnosis

Speed is of the essence in many psychological crises. AI models, trained on
vast datasets, can assist in rapidly analyzing patient information to provide
preliminary diagnoses. This rapid assessment can then guide professionals in
crafting immediate intervention strategies or treatments. Also the AI model,
trained using large-scale data, is expected to accurately identify medical
conditions, aiming to reduce the occurrences of misdiagnosis and missed
diagnosis.

##### Treatment (cognitive behavioral therapy, communication dialogue,
companionship)

  * •

Cognitive Behavioral Therapy (CBT): AI-driven platforms can offer personalized
CBT sessions, adjusting strategies based on real-time feedback and
progression.

  * •

Communication and Dialogue: Virtual AI therapists can facilitate round-the-
clock communication, providing support during moments of acute distress.

  * •

Companionship: AI companions can offer a sense of comfort, especially to
individuals battling isolation, helping them navigate emotional terrains.

##### Behavior recognition

Harnessing AI’s pattern recognition capabilities can be transformative in
identifying critical behaviors. Whether it’s detecting early signs of
impulsiveness in someone prone to self-harm or identifying potential choking
hazards for patients with certain disorders, AI can provide real-time alerts
and interventions.

##### Supervision and continuous care

Post-treatment supervision is paramount in ensuring long-term well-being. AI-
powered systems can monitor patients’ progress, adherence to medication or
therapy regimes, and any potential relapses. By providing real-time insights
to caregivers or medical professionals, these systems ensure that individuals
receive timely support and care adjustments.

###  6.2 Generalist AI across varied psychological scenarios

The integration of generalist AI into the realm of psychology extends far
beyond traditional therapeutic spaces. The capability of AI to adapt and
function across varied environments underscores its versatility. This chapter
delves into the prospective roles of AI in diverse settings and the potential
benefits it could usher into the field of psychology.

##### Hospital

In a hospital setting, the precision and efficiency of AI can be monumental.

  * •

Patient Intake: AI can streamline the intake process, quickly assessing
patients’ psychological states, prioritizing cases based on urgency, and
providing preliminary insights to medical professionals.

  * •

Treatment Recommendations: By analyzing patients’ medical history and current
psychological assessments, AI can suggest potential treatment pathways,
enhancing the decision-making process for clinicians.

  * •

Post-treatment Monitoring: AI tools can assist in tracking patients’ recovery,
noting any anomalies or potential relapse indicators.

##### Community

AI’s role in community settings is multifaceted, encompassing both prevention
and intervention.

  * •

Mental Health Awareness Campaigns: AI can analyze community-specific data to
design targeted mental health awareness campaigns, addressing unique regional
challenges.

  * •

Early Detection: Through community health drives and surveys powered by AI,
early signs of psychological distress can be identified, ensuring timely
interventions.

  * •

Support Groups: AI can help curate and manage community-based support groups,
connecting individuals with similar experiences and facilitating therapeutic
discussions.

##### Family

Within the family setting, AI can act as a bridge, fostering communication,
understanding, and support.

  * •

Home-based Therapy: AI-driven platforms can offer personalized therapy
sessions, making psychological support accessible from the comfort of one’s
home.

  * •

Conflict Resolution: AI tools can assist families in navigating conflicts,
providing communication strategies and promoting understanding.

  * •

Caregiver Support: For families with members undergoing psychological
treatments, AI can offer resources, coping mechanisms, and real-time
assistance to caregivers.

##### School

Schools, as primary developmental environments, can greatly benefit from AI’s
capabilities.

  * •

Student Monitoring: AI systems can analyze students’ behaviors, academic
performances, and interactions to detect early signs of psychological
distress.

  * •

Personalized Counselling: AI-driven platforms can offer preliminary counseling
sessions, guiding students through academic stresses, peer issues, or personal
challenges.

  * •

Educational Programs: AI can curate psychological education modules, teaching
students about mental health, self-care, and resilience.

##### Social media

In the digital age, the impact of social media on psychology is undeniable,
making its intersection with AI especially pertinent.

  * •

Mental Health Check-ins: AI tools can analyze users’ posts, comments, and
interactions to detect signs of distress or potential cries for help,
subsequently offering resources or alerting loved ones.

  * •

Positive Content Promotion: AI algorithms can promote positive, uplifting
content, fostering a healthier online environment.

  * •

Bullying and Harassment Detection: AI can identify and mitigate instances of
online harassment, ensuring safer digital spaces for users.

###  6.3 Tailoring psychological Generalist AI across age groups

##### Children: Early Detection of Psychological Issues

Childhood is a foundational period, and timely psychological interventions can
shape healthier adult lives.

  * •

Behavioral analytics: AI systems can analyze patterns in children’s play,
interactions, and academic performances, flagging potential developmental or
psychological concerns.

  * •

Interactive learning platforms: AI-driven educational tools can be designed to
both teach and gauge psychological well-being, ensuring that learning and
mental health go hand in hand.

  * •

Parental guidance: AI can provide resources, insights, and real-time guidance
to parents, helping them navigate their child’s psychological needs.

##### Adolescents

Adolescence is marked by rapid change, making tailored psychological support
crucial.

  * •

Emotional well-being apps: AI-driven applications can offer adolescents a
platform to understand and navigate their emotions, providing coping
mechanisms for stresses tied to academics, peer pressure, identity crises, and
more.

  * •

Online therapy platforms: Recognizing the digital affinity of this age group,
AI can power online therapy platforms, offering adolescents an accessible and
less intimidating avenue for seeking help.

  * •

Peer interaction analysis: AI tools monitoring online interactions can detect
signs of bullying, depression, or other psychological challenges, paving the
way for timely interventions.

##### Adults

Adult life, with its myriad responsibilities, brings unique psychological
challenges that AI can address.

  * •

Work-life balance tools: AI-driven platforms can assist adults in managing
stress, ensuring a healthier work-life balance.

  * •

Relationship and family counseling: AI systems can offer guidance on
relationship challenges, parenting, and family dynamics.

  * •

Mental health monitoring: Through wearable tech and mobile apps, AI can
continually assess an adult’s mental well-being, offering resources or
interventions when necessary.

##### Elderly

  * •

Memory and cognitive support: AI tools can offer cognitive exercises and
memory aids, helping combat age-related cognitive decline.

  * •

Emotional companionship: AI-powered companion robots or virtual assistants can
offer conversation, alleviating feelings of loneliness and isolation.

  * •

Health monitoring: Beyond mental well-being, AI tools can holistically monitor
an elderly individual’s health, detecting any psychological distress signs in
tandem with physical health anomalies.

###  6.4 Generalist AI in stages of psychological disease

The continuum of psychological disease is complex, often marked by subtle
beginnings, acute crises, and prolonged recovery phases. The application of
generalist AI holds great promise in comprehensively addressing these stages,
from prevention to monitoring relapses. This chapter highlights the potential
interventions AI can facilitate across various disease stages, ensuring
timely, effective, and empathetic care.

##### Disease prevention

Before the onset of a psychological disorder, AI can play a pivotal role in
recognizing predispositions and initiating preventive measures.

  * •

Lifestyle recommendations: Based on individual data and wider psychological
research, AI can suggest lifestyle choices that optimize mental well-being.

  * •

Regular mental health screenings: AI-powered platforms can offer regular mood
and mental health assessments, ensuring early detection of distress.

  * •

Educational content: AI systems can curate and recommend informative content
on mental well-being, bolstering public awareness and preventive knowledge.

##### Early warning

Detecting signs at the nascent stages of psychological distress can
drastically improve treatment outcomes.

  * •

Behavioral pattern analysis: By analyzing behavioral data from wearables,
mobile apps, or online interactions, AI can identify subtle changes indicative
of emerging psychological issues.

  * •

Personalized alerts: AI-driven platforms can notify individuals and their
caregivers about detected changes, suggesting further assessments or
interventions.

  * •

Resource recommendations: Based on detected early warning signs, AI can
recommend relevant self-help resources or therapeutic interventions.

##### Crisis intervention: suicide prevention

In moments of acute distress, timely interventions are life-saving. AI holds
the potential to act swiftly in such crises.

  * •

Sentiment analysis: AI tools can scan social media, messages, or other digital
platforms for signs of severe distress or suicidal ideation, triggering
immediate alerts to caregivers or emergency services.

  * •

Immediate support chatbots: AI-driven chatbots can offer immediate emotional
support, providing a bridge until human intervention becomes available.

  * •

Safety planning: For individuals identified at risk, AI can assist in crafting
personalized safety plans, ensuring resources and support are readily
available..

##### Long-term rehabilitation monitoring

Post-crisis, the recovery phase is crucial, necessitating consistent
monitoring and support.

  * •

Daily mental health check-ins: AI-powered platforms can facilitate daily
emotional and mental assessments, tracking recovery progress.

  * •

Therapeutic activity suggestions: Based on recovery progress, AI can recommend
therapeutic activities, exercises, or relaxation techniques.

  * •

Caregiver and therapist communication: AI systems can relay progress reports
to therapists or caregivers, ensuring all stakeholders are informed and
aligned.

##### Disease relapse

Ensuring that an individual doesn’t revert to a previously overcome
psychological challenge is vital.

  * •

Behavioral trend analysis: AI can detect patterns similar to those exhibited
during prior episodes of distress, signaling potential relapses.

  * •

Immediate therapeutic interventions: At signs of relapse, AI-driven platforms
can offer immediate therapeutic resources or establish contact with
therapists.

  * •

Relapse education: AI tools can provide information on recognizing and
managing relapse symptoms, empowering individuals in their ongoing recovery
journey.

##  7 Perspectives in practical application

As the realm of generalist AI continues to burgeon, it becomes vital to
consider its practicality and implications in real-world settings. The
following sections delve into the feasibility of employing large-scale AI
models, the necessity of multi-domain verification, ethical concerns, and
privacy considerations.

###  7.1 Leveraging open source pre-trained models

Training expansive AI models from the ground up is both resource-intensive and
economically challenging, especially for institutions like hospitals or
smaller enterprises. Given the substantial investments in terms of time,
computing power, and finances, it’s often untenable for such organizations to
embark on this venture independently. The solution? Tuning based on open-
source pre-trained models. This approach not only optimizes resources but also
taps into the collective knowledge of the broader AI community. Given the
trajectory of technological advancements, fine-tuning pre-existing models
emerges as a pragmatic direction for the widespread adoption of AI
capabilities.

###  7.2 The imperative of multi-domain evaluation

The evolution and success of LLMs, evident in today’s technological landscape,
shine a spotlight on the potential of generalist AI models. However, potential
alone isn’t enough. The efficacy of these models must be tried and tested
across a diverse array of domains and scenarios. By doing so, we demystify
their boundaries, ascertain their capabilities, and determine their real-world
relevance. This extensive verification becomes an invaluable roadmap, guiding
researchers and developers alike in harnessing the true power of generalist
AI. Simply put, to realize the dream of a universally applicable AI, there is
no bypassing the rigorous route of multi-domain and multi-scenario
verification.

###  7.3 Navigating the ethical maze

While the technical advancements in AI are noteworthy, they are not devoid of
ethical quandaries. Issues of fairness, transparency, and bias persistently
intertwine with AI’s decision-making processes. Beyond the binary of
algorithms, these concerns touch upon societal fabric, underscoring the
importance of cultural and regional nuances. To ensure AI models serve
everyone equitably, there’s an inescapable need for continuous monitoring,
adjustments, and evaluations. By remaining vigilant against inherent biases
and championing transparent methodologies, the AI community can lay the
foundation for ethically robust systems [101].

###  7.4 Safeguarding permissions and privacy

In an era where data is likened to gold, the stakes for protecting it are
equally high. The integration of AI into various sectors necessitates
stringent privacy protocols. The sanctity of personal information, be it
medical records in hospitals or private chats on social media, must be
preserved. Navigating the balance between AI’s insatiable thirst for data and
an individual’s right to privacy remains one of the pivotal challenges in its
practical application. As AI continues to intertwine with daily life, it
becomes paramount to construct systems that prioritize user privacy, ensuring
data utilization with consent and purpose.

The journey towards the seamless integration of generalist AI in practical
scenarios is rife with challenges, both technical and ethical. However, by
addressing these concerns head-on and leveraging the collective knowledge of
the community, we move closer to an era where AI isn’t just a tool but a
trusted companion in various spheres of life.

##  8 Conclusion

In our paper, we conduct a comprehensive review of model evaluation and the
specific applications of foundation models, particularly LLMs, in the field of
psychology. Our analysis emphasizes the significance of multi-faceted
verification, categorizing and summarizing relevant literature by its type and
application direction. Given the extensive discussions LLMs have sparked in
related fields, we also collate and present various viewpoints from pertinent
literature. Looking ahead, we identify potential directions for the
advancement of generalist AI in psychology, focusing on four key aspects: the
psychotherapy process, application scenarios, age groups, and the disease
process. Furthermore, we delve into the critical concerns regarding the
practical application of LLMs. These concerns start with security and extend
to advocating for open-source and transparent models, multi-domain evaluation,
adherence to ethical frameworks, and addressing privacy issues to establish
clear system boundaries. Our paper not only consolidates current literature
but also offers in-depth analysis and exploration of future developments from
multiple perspectives. This comprehensive approach aims to chart potential
pathways for progress in this field and towards the development of a
psychological generalist AI.

##  9 Abbreviation

  * •

AI: Artificial Intelligence

  * •

APA: American Psychiatric Association

  * •

CBT: Cognitive Behavioral Therapy

  * •

CLEVA: Chinese Language Models EVAluation Platform

  * •

GMAI: General Medical Artificial Intelligence

  * •

GPT: Generative Pretrained Transformer

  * •

GPAI: Generalist Psychology Artificial Intelligence

  * •

LEAS: Level of Emotional Awareness Scale

  * •

LLM: Large language model

  * •

NLP: natural language processing

  * •

ToM: Theory of Mind

  * •

WMGPT: Well-Mind ChatGPT

##  10 Acknowledgments

This work was supported by grants from the National Natural Science Foundation
of China (grant numbers:72174152, 72304212 and 82071546), Fundamental Research
Funds for the Central Universities (grant numbers: 2042022kf1218;
2042022kf1037), 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).

##  11 Authors Contributions

Guanghui Fu, Bing Xiang Yang, and Jianqiang Li were instrumental in
conceptualizing the idea and structure of the paper. Tianyu He, Fan Wang,
Guanghui Fu, Yijing Yu, Changwei Song and Hongzhi Qi undertook the task of
material preparation and literature collection. The initial draft of the
manuscript was composed by Tianyu He and Guanghui Fu. Bing Xiang Yang,
Jianqiang Li, Qing Zhao, Dan Luo, and Huijing Zou contributed to the article
through their review and editing efforts. All authors engaged in providing
feedback on previous versions of the manuscript. The final draft was read and
approved by all contributing authors.

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