| Journal of Smart Sensors and Computing
Received: 20 June 2025; Revised: 17 August 2025; Accepted: 01 September 2025; Published Online: 04 September 2025.
J. Smart Sens. Comput., 2025, 1(2), 25208 | Volume 1 Issue 2 (September 2025) | DOI: https://doi.org/10.64189/ssc.25208
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
A Review of Recent Advancements in Healthcare
Chatbots
Vibhav V. Sinai Pissurlenkar,
1,
* Basabdatta Sen Bhattacharya
2
and Baskar Sundarrajan
3
1
Infuse Consultancy Ltd., Panaji, Goa, 403001, India
2
BITS Pilani, Goa Campus, Vasco, Goa, 403726, India
3
Goa Business School, Goa University, Panaji, Goa, 403206, India
*Email: vpissurlenkar23@gmail.com (Vibhav V. Sinai Pissurlenkar)
Abstract
The quick development of healthcare chatbot technologies, especially since the Coronavirus disease 2019
(COVID-19) pandemic broke out, has been a critical transformation of how healthcare services are presented
and consumed. This is significantly fueled by the development of Natural Language Processing (NLP) and the
emergence of Large Language Models (LLMs) such as ChatGPT, which have made more advanced, precise, and
human-friendly conversational interfaces possible. Between 2023 and mid-2024, the healthcare field
experienced an increase in chatbot usage across a range of areas, particularly in mental and overall healthcare.
Chatbots have become great assets for simplifying administrative work, delivering preliminary consultations,
helping with symptom checkers, delivering mental health services, and supporting education for patients. In
mental health, for instance, chatbots can provide a degree of accessibility and anonymity that is highly effective
in breaking down stigma, getting mental health care closer to people. In general healthcare, too, these systems
have played a key role in easing the workload on healthcare professionals, triaging patients, making health
information available 24/7, and even aiding chronic disease management through tailored health advice. Even
with their increasing promise, however, there are major hurdles to successfully integrating chatbots into
healthcare systems. Some of these include data privacy and security concerns, the risk of wrong diagnosis or
recommendation, and the limited capacity of chatbots to address complicated or sensitive patient needs that
demand human judgment. Moreover, there are governance and ethical concerns with the application of Artificial
Intelligence (AI) in health care, especially regarding achieving fairness, transparency, and accountability. As
sophisticated as these systems become, their widespread adaptation and harmonious integration into health
care processes is a complicated and continuous process, with user trust and system interoperability shortfalls
continuing to present hurdles. Yet, with ongoing improvements in AI and NLP, chatbots have massive potential
to revolutionize the delivery of healthcare by offering more personalized, effective, and accessible care.
Keywords: Artificial intelligence in healthcare; Healthcare automation; Chatbots; Conversational agents; Large
Language Models.
1. Introduction
In the post-COVID pandemic era, the potential of Artificial Intelligence (AI) in various domains of daily life has
become increasingly evident. In particular, the healthcare sector has felt significant strain on its workforce
during the pandemic, highlighting the urgent need for AI integration within this narrow domain. Chatbot, also
known as conversational agent, have demonstrated considerable promise, particularly due to their features
such as ease of access and convenience. These tools allow individuals to engage remotely at their own pace,
enabling them to seek assistance for various purposes, including education, therapeutic support, diagnostics,
and treatments in both mental and general healthcare. As healthcare systems continue to adapt to the evolving
landscape, a chatbot can help bridge gaps in service delivery, enhance patient engagement, and alleviate some
of the burdens on healthcare professionals.
Chatbots, including large language models (LLMs), have swiftly integrated into our daily routines. Various
organizations around the world, including governments, have recognized their potential and necessity. For
instance, some studies, such as those cited in U.S. Government Accountability Office
[1]
, conducted background
research during the early stages of the COVID-19 pandemic. More recently, certain governments, such as the UK,
have established regulations for the use of AI in this specialized domain.
[2]
Many individuals have also
acknowledged the capabilities of these technologies; for example, a Forbes article by Haseltine
[3]
tested the
accuracy of responses from ChatGPT versions 3.5 and 4 on general medical questions.
Furthermore, an article on Medium by Katendejericho
[4]
discusses the use of chatbots in mental health,
highlighting their advantages, adverse effects, and ethical considerations. The abundance of such materials
available online and increase research in the bespoke domain indicates a growing awareness of the potential
benefits and challenges associated with AI in healthcare. As these technologies continue to evolve, it is
imperative to critically assess their effectiveness, safety, and ethical implications. The abundance of such
materials available online and the increase in research in this domain indicate a growing awareness of the
potential benefits and challenges associated with AI in healthcare, as illustrated in Fig. 1.
Fig. 1: Rising expectations for tech-driven healthcare solutions post-pandemic, reflected in increased research.
In this review, we have examined various chatbots in the healthcare domain published in the year 2023 to mid-
2024. These chatbots utilize AI technology in various forms, including primitive machine learning algorithms
such as (decision tree) and more advan
LLMs within this context based on some of the research articles published during the specified time frame. This
article overall reviews the advancements in chatbots and the role of LLM. With these findings, we aim to provide
a comprehensive overview of the latest advancements in AI-powered chatbots in healthcare and their potential
to shape future practices.
2. Literature review
Chatbot development has evolved significantly since the creation of ELIZA in 1966, one of the first natural
language processing (NLP) systems, which employed basic pattern matching techniques to simulate
erian psychotherapist, offering a glimpse into the
potential of human-computer interaction, as discussed by Weizenbaum.
[5]
ELIZA, despite its simplicity, laid the
foundation for future developments in chatbot technology. Later, PARRY (1972), developed by Colby,
[6]
advanced
the field by simulating the thought patterns of a paranoid schizophrenic. PARRY was an important step forward,
demonstrating that chatbots could simulate not only structured conversations but also complex psychological
profiles. The 1990s marked a shift toward more sophisticated rule-based systems, such as ALICE (1995), created
by Wallace.
[7]
ALICE utilized an extensive set of predefined rules to engage in meaningful conversation with
users, and it earned recognition through its success in the annual Loebner Prize competition. The system relied
on pattern matching and heuristic techniques to facilitate conversations, providing an early look into how
chatbots could be used for more diverse purposes beyond simple conversation simulation. The development of
machine learning and statistical models in the 2000s brought about major breakthroughs. Cha
Siri (2011) harnessed voice recognition and natural language understanding, making it easier for users to
interact with technology through simple voice commands. These early machine learning-based chatbots marked
a significant leap in terms of practical application and user engagement. The most recent advancements, such
[8]
have further transformed the landscape by leveraging deep learning
and vast datasets. These models have dramatically improved chatbot fluency, context understanding, and the
overall quality of interactions, making them more capable of handling diverse and complex tasks.
2.1 Chatbots in healthcare- A growing trend
The integration of chatbots in healthcare has garnered significant attention, largely driven by the increasing
prevalence of mental health conditions and a shortage of healthcare professionals, particularly in under-
resourced areas. According to Ahmed et al.,
[9]
the application of chatbots in mental health support has proven to
be valuable, particularly in managing conditions like anxiety and depression. Their scoping review, based on the
PRISMA methodology, analyzed 42 studies and identified several chatbots, including Woebot and Wysa, which
leverage Cognitive Behavioral Therapy (CBT) techniques to assist patients in managing their mental health.
Despite their growing use, the authors highlight a gap in large-scale usage data and clinical trials, suggesting the
need for more comprehensive evaluations and research to better understand their effectiveness in real-world
scenarios. Additionally, there is a notable lack of chatbot interventions aimed at general health care. A review
by Phiri et al.
[10]
examined the use of chatbots in healthcare across Africa from 2017 to 2022, identifying 12
relevant studies. The review notes that chatbots in Africa have primarily focused on educational purposes,
particularly on social media platforms like Facebook and WhatsApp. These chatbots educate users on essential
health topics such as vaccinations, contraception, and HIV testing. Notably, chatbots targeting HIV prevention
and testing have been particularly impactful in reaching younger demographics, demonstrating the potential
for chatbots to address critical health issues in resource-limited settings. However, the review also highlighted
challenges such as technical barriers, low internet penetration, and trust issues among users, which significantly
influence the adoption of health-related chatbots in the region. The findings also reveal a gap in research on
user experience with health chatbots, particularly in rural areas where access to healthcare services is limited.
While studies suggest that digital technologies could increase healthcare access in these regions, they also point
to the absence of effective evaluation frameworks. As van Heerden et al.
[11]
discussed, chatbots like Amanda
Selfe and Nolwazi-designed for HIV prevention and self-testing-show promise in terms of improving healthcare
interactions. The use of an isiZulu-speaking chatbot (Nolwazi) for HIV self-testing, for instance, was preferred
by 80% of participants over human counselors, especially among men. These findings highlight how chatbots
can facilitate sensitive healthcare discussions, including those on sexually transmitted infections (STIs), pre-
exposure prophylaxis (PrEP), and sexual health, which are often stigmatized in many communities. However,
despite these positive developments, challenges remain. The integration of chatbots into healthcare systems
raises critical concerns about safety, privacy, and ethics. While LLM chatbots offer advanced capabilities, their
deployment in healthcare environments requires rigorous validation to ensure accuracy, trustworthiness, and
compliance with medical standards. Rule-based chatbots, in contrast, provide greater control over responses
and can be fine-tuned to meet specific needs, making them a preferable option for certain applications in
healthcare.
2.2 Future directions & challenges
The literature on healthcare chatbots reveals a growing interest in their use for both mental health support and
chronic illness management. However, the field still faces several barriers, particularly in the areas of clinical
validation and user engagement. A more extensive body of research is required to establish the effectiveness of
these technologies in real-world healthcare settings, particularly in underserved and rural areas where chatbots
could have the greatest impact. Future research should focus on developing robust evaluation frameworks and
conducting large-scale clinical trials to assess the long-term efficacy and safety of healthcare chatbots. Moreover,
the integration of AI and machine learning into healthcare chatbots raises important questions about data
security, user trust, and the potential for algorithmic biases. As chatbots become increasingly sophisticated,
ensuring that they operate ethically and safely will be paramount. Researchers should also explore how to
optimize chatbot interfaces to better meet the needs of diverse patient populations, including those with low
health literacy or those from marginalized communities. In conclusion, while chatbots in healthcare present a
promising solution to many challenges faced by healthcare systems worldwide, significant work remains to
ensure their effectiveness and safety. The development of comprehensive evaluation framework, coupled with
a focus on user engagement and trust, will be essential in realizing the full potential of chatbots in healthcare.
3. Methodology
For this review, the research methodology used is Systematic Literature Review (SLR), with a focus on
inclusion/exclusion criteria and quality assessment. Given brevity, the exact methodology used that aligns with
the core aspects of SLR, encompassing the search strategy, curation, quality assessment, and final selection
criteria, is as follows.
For this review, databases such as Google Scholar, OpenAlex, CrossRef and Scopus were searched using
-24. A total of
3003 records matched our keyword search across all the above-mentioned databases. Further curation was
done based on citation score while also eliminating duplicates, bringing the total number down to 900. We
discarded 420 publications that were not relevant to our objectives for this review, this scrutiny was done based
on the title. To ensure a rigorous quality assessment process, we used a custom written python code to retain
publications from well recognized Journals and having good impact factors viz, Nature, Elsevier, Springer,
Frontiers, PLOS, IEEE, ACM, JMIR, Taylor & Francis, Sage, Jama network, Wiley, MDPI. Eligibility criteria for
curation and selection of research publications were whether, in the bespoke domain, the developed chatbot
provide any assistance to users in terms of education, assistive care or counselling and the use of AI technology
in the development of the chatbot has been explicitly mentioned in the research article. The publications that
did not belong to any of these were discarded. Finally, only 44 articles were retained that fell directly into our
topic of interest.
Table 1 gives details on the different chatbots explored in this review, for the benefit of the readers only those
chatbots on which some form of testing is conducted and/or available for public usage are included.
Table 1: Overview of Chatbots tested, and/or publicly available for use.
Genre
Chatbot
Testing
Available for Public
Mental
Health
Fido
Randomized Control Trial (RCT) conducted
with 81 participants
No
Chatbot in Malawi
RCT conducted, 355 participants used the
chatbot
No
Aroha
RCT conducted with 15 participants
No
Chatbot for psychiatric
guidance for
Methamphetamine
addicted patients
RCT conducted, with 6
months follow-up on 55
participants
No
Besty
RCT conducted on 45 participants
No
Vickybot
Clinical scenarios tested with 17 participants
No
Emohaa
Testing conducted, with 142 participants
using the chatbot
No
Chatpal
RCT was conducted previously, Conducted
log analysis recently of 1403 participants
Available through official
website
(Mobile application)
Wysa
RCT conducted with 68 participants
Available through official
website
(Mobile application)
Woebot
RCT conducted, 68 participants utilized the
app
Available through official
website
(Mobile application)
Moodfit
Unknown
Available through official
website
(Mobile application)
Physical
Health
Nena
Acceptance testing conducted with 301
participants
No
Saytù Hemophilie
Usability testing conducted with 57
participants
No
Lucy Liverbot
Beta Testing conducted on 20 participants
No
PROSCA
Testing conducted on 10 participants
No
Haris
Beta testing conducted with 14 participants
No
Smart Monitoring Tool
(SMT, IOT based
intervention integrated
chatbot)
Testing conducted with 13 participants
No
4. Chatbots in healthcare
Chatbots and virtual humans are becoming increasingly prevalent in healthcare, especially within mental health
interventions. When evaluating these technologies, it is essential to consider these key aspects: a) AI Enabled:
Are the interventions AI based? b) Evidence from Testing Trials: Have randomized or clinical trials provided
evidence of the effectiveness of these interventions? c) Public Deployment: Are these chatbot deployed for
public use? In the following subsections we are going to explore different advancements in mental health and
general health.
4.1 Chatbots in mental health
Recent novel advances in mental health include a chatbot developed using Reinforcement Learning with Human
Feedback (RLHF) aimed at enhancing mental health therapy.
[12]
Although many chatbots are grounded in
Cognitive Behavioral Therapy (CBT), which effectively addresses issues such as depression and anxiety, the
RLHF approach allows for more adaptive and nuanced interactions. This method enables the chatbot to improve
its response accuracy overtime through incremental conversations, contrasting with traditional rule-based
chatbots that rely on fixed responses. Although the proposed chatbot demonstrated acceptable performance
metrics such as naturalness, coherence, engagement, and understandability, the authors noted a lack of
actionable plans for medical implementation. The study also raised concerns regarding the absence of clinical
trial data or user demographic testing, indicating a need for further validation before the chatbot can be deemed
-
coherence (response is coherent in dialogue history) and understandability (is response understandable). The
naturalness score obtained post evaluation is 0.94, coherence is 0.96 and understandability is 0.93. Since the
authors have claimed that no other research has used this matrix for evaluation, it is difficult to compare it with
other work.
Several chatbots have been evaluated through randomized controlled trials (RCTs). With participants recruited
within age group of 18-35 years, Karkosz et al.
[13]
present Fido, a machine learning-based chatbot designed to
assist young adults with anxiety and depressive symptoms using cognitive behavioral therapy (CBT) techniques.
Developed through iterative co-development with therapists and potential users, Fido underwent rigorous
quality assurance and testing, although no features were modified during the trial. The chatbot employs the ABC
technique from CBT to help users distinguish between activating events, beliefs, and their emotional or
behavioral consequences. In a randomized controlled trial involving 81 participants, those using Fido reported
significant reductions in depression, anxiety, and worry symptoms, alongside increases in life satisfaction and
positive effects, with effects lasting for at least a month. Despite its promising results, the study does not clarify
whether Fido is available as a standalone mobile app or a web-based service. Additionally, there is no indication
of plans for public release, as participants were individually added as testers via an email link, raising questions
about its broader accessibility.
With participants in age group 18-29 years, Kleinau
[14]
conducted a randomized controlled trial in Malawi to
evaluate during the COVID-19 pandemic. The study targeted various professional cadres, including doctors,
nurses, and clinical officers, to address mental health challenges such as depression, anxiety, and stress in a
region with limited access to mental health resources. Out of the 481 participants in the control group, only 355
Malawi, where access to psychiatrists and other mental health professionals is scarce. A total of 820 participants
completed a participant experience questionnaire, with 37% from the control group and 63% from the
treatment group. In the treatment group, 50% used the app for over 28 days, with 91% finding it easy to use
and 92% deriving benefits. Common issues included confusion with the trial welcome email (32%) and
difficulties with app setup (27%). The Net Promoter Score (NPS) was 55. In the control group, 52% used the
Internet resources for over 15 days, with 87% finding them easy to use. The NPS for the control group was 51.
Both groups encountered similar challenges with time and content complexity. Although the study focused on
healthcare workers, it highlights the potential for chatbot interventions to address mental health needs in
under-resourced areas, emphasizing the importance of accessible mental health support for those on the front
lines.
Tested with participants in mean age group of 20, Kang et al.
[15]
explored the Aroha chatbot, developed during
the COVID-19 pandemic to assist youth (ages 13-24) in managing stress during lockdowns. Aroha employs a
rule-based or decision tree algorithm to suggest calming activities and practical advice. In a study involving 15
participants (2 males, 13 females), feedback was gathered to refine the application. However, the authors noted
that most participants did not use Aroha in their daily lives outside the controlled environment. This raises
questions about its usability and effectiveness on a broader scale for stress management among the general
population. Aroha was generally well-received by participants for its conversational tone, use of Kiwi slang, and
relatable language, which helped users feel more comfortable. However, participants found the chatbot
overwhelming with too much text at once and expressed frustration with its limited understanding of free-text
-being advice, and accessibility
were praised, with participants appreciating its ability to offer support without the stigma or barriers associated
with traditional mental health services. The insights gained from this study highlight the potential for
improvement and the importance of real-world applicability in developing chatbot for mental health support.
Participants between 18 and 65 years were recruited by Chun-Hung et al.
[16]
developed a chatbot using machine
learning and natural language processing (NLP), deployed on the Line chatbot platform, to assist in the
treatment of patients at Jianan Psychiatric Centers. During the preliminary deployment, focus group members
and case managers interacted with the chatbot in a realistic setting to simulate the eventual user experience.
After analysing feedback, the chatbot underwent refinements to optimize functionality and user interaction. A
total of 33 participants were included in the treatment group, with 25 remaining in the control group out of an
initial 137. In a study with 50 participants in the (chatbot-assisted treatment) CAT group and 49 in the control
group, the CAT group had fewer (Methamphetamine) MA-positive urine samples (19.5% vs. 29.6%). MA-
positive samples were positively correlated with frequency of MA use, severity of use disorder, and
polysubstance use, and negatively correlated with readiness to change. At the 6-month follow-up, 55
participants completed the study, with 60% reporting relative satisfaction. While the experimental group
showed slightly higher treatment retention and significantly fewer MA-positive urine samples than the control
group, no significant clinical differences were observed. The study suggests that chatbots can provide
immediate support, collect valuable clinical data, and monitor outcomes without significantly burdening
patients or providers. Participants generally expressed satisfaction with receiving CAT.
Participants, aged 24 to 68 were recruited by Thunström et al.
[17]
who conducted a randomized controlled trial
comparing usability between an anthropomorphic digital human and a text-based chatbot, BETSY (Behaviour,
Emotion, Therapy System, and You), among healthy participants (n = 45). Participants were selected based on
their scores on the Generalized Anxiety Disorder (GAD-7) scale, were divided into two groups: one interacted
with a text-only version of BETSY and the other with a voice-activated digital human. Notably, men were less
likely to report annoyance with BETSY compared to women. Overall, the trial found a slight bias toward the text-
only interface in terms of acceptability and usability; however, the digital voice-based interface was still highly
rated among participants. This study contributes to understanding user preferences in chatbot, suggesting that
while text interfaces may be favoured for usability, voice-based interactions hold significant potential.
In the study by Anmella et al.
[18]
, participants with a mean age of 3537 years were recruited to evaluate the
Vickybot chatbot. Vickybot is designed to assist healthcare professionals and patients experiencing anxiety-
depressive symptoms and work-related burnout. This mobile intervention included self-administered scales for
monitoring anxiety (GAD-7), depression (PHQ-9), and burnout (using items from the Maslach Burnout
Inventory) every two weeks. Psychological modules tailored to assessment severity were delivered, covering
anxiety, depression, and work-related stress, based on eclectic therapy, including CBT, mindfulness, and
dialectical behavioral therapy. A chatbot guided users through modules, addressed queries, and identified
emergencies like suicide thoughts, triggering alerts for immediate assistance. Reminders supported weekly
objectives and biweekly assessments, while users could also record audio reflections for potential voice analysis.
This comprehensive system ensured personalized, proactive mental health management and emergency
response. This research is part of the PRESTO project, which aims to combine machine learning models for
severity assessment with a smartphone-based intervention for screening, monitoring, and treatment delivery.
The primary objective of the study was to evaluate the feasibility of the intervention, while secondary aims
focused on its effectiveness in reducing symptoms and detecting suicide risk. During the setup phase, 40 users
tested Vickybot, confirming reliable data transmission and server performance. In the simulation phase, 17
(76% female) users tested clinical scenarios, with 98.5% of expected functions and 98.8% of expected modules
successfully applied. Usability scored high (mean 6.39/7), with improvements needed in reminders,
personalization, and chatbot comprehension. In the feasibility and effectiveness study conducted, from among
130 invited participants, only 34 signed up, reporting anxiety (100%), depression (94%), and burnout (65%).
Vickybot demonstrated usability, satisfaction, and acceptability but highlighted areas for enhancement. Notably,
the authors report that Vickybot successfully identified emergency situations involving suicidal thoughts,
facilitating timely interventions. However, while the chatbot showed effectiveness in alleviating work-related
burnout, its impact on anxiety and depression was less pronounced. Importantly, the Vickybot app does not
appear to be publicly available for download, indicating that further development and testing are required
before it can be widely implemented as a mental health support tool.
The average age of the studied sample was 30.90 years for Emohaa explored by Sabour et al.
[19]
.
Emohaa is a
mental health chatbot designed to reduce mental distress among users in China, available on WeChat. The
chatbot comprises two main platforms: Cognitive Behavioral Therapy Chatbot (CBT-Bot): This rule-based
component follows CBT principles, providing users with exercises like automatic thinking corrections and
guided expressive writing. Users select options in scenarios and report their mood after completing exercises.
Emotional Support Chatbot (ES-Bot): This AI-driven version employs a BERT-based model, generating
messages to identify signs of suicidal thoughts, prompting appropriate emergency responses. The study found
significant reductions in depression, negative affect, and insomnia among users of Emohaa, measured by the
PHQ-9, PANAS, and ISI questionnaires. Participants, all from Mainland China, had an average of 7.87 years of
work experience (SD = 8.45). Baseline mental distress levels were moderate, with depression (PHQ-9: M =
16.43, SD = 5.01), anxiety (GAD-7: M = 16.23, SD = 4.37), and insomnia (M = 16.45, SD = 5.38). Positive and
negative effects were assessed using the PANAS, with participants showing moderate positive effect (M = 24.76,
SD = 7.20) and negative effect (M = 22.34, SD = 6.35). ANOVA and chi-squared tests were used to examine
differences in baseline variables (age, gender, PHQ-9, GAD-7, PA, NA, insomnia) among the three groups: control,
CBT bot, and ES bot. The results indicated no significant differences in baseline demographics (age: F = 2.17, p
= 0.117; gender: X² = 3.56, p = 0.173) or mental distress variables (PHQ-9: F = 2.45, p = 0.088; GAD-7: F =0.93,
d its potential as a valuable resource for mental
addressing mental health issues, making it an essential reference for our work.
The chatbots available for public use are as follows: In a series of studies conducted under the Northern
Periphery and Arctic Programme [NPAP],
[20]
the ChatPal project proposed a non-commercial chatbot available
as an Android and iOS app, primarily targeting the mental health and well-being of rural populations. Although
ChatPal was developed prior, several recent studies have been submitted regarding the chatbot, as follows: In
study published by Potts et al.
[21]
on ChatPal, a multilingual digital mental health chatbot available in English,
Scottish Gaelic, Swedish, and Finnis, involved a multicenter pre-post intervention de sign with 348 participants,
utilizing standardized outcome measures such as the Short Warwick-Edinburgh Mental Well-Being Scale and
the World Health Organization-Five Well-Being Index. Evaluated at baseline, midpoint, and endpoint, the results
indicated that ChatPal has the potential to complement other digital and face-to-face services in promoting
mental well-being. However, the authors emphasized the need for further research to assess the effectiveness
of the methods employed. This highlights the growing trend toward multilingual and culturally inclusive mental
health support solutions, catering to diverse populations. Booth et al.
[22]
conducted a study analysing user event
logs for the ChatPal mental health and well-being chatbot, focusing on usage patterns and feature associations.
Utilizing a k-means clustering algorithm, the researchers examined anonymized login data from 1,403 users
between January 24, 2022, and June 22, 2022, ultimately narrowing their analysis to 579 adult users over 18
years old. Among these, 348 participants were specifically recruited for a 12-week pre-post study, with
approximately 67 percent identifying as female. The analysis revealed three distinct user clusters: abandon,
sporadic, and transient users, providing insights into engagement levels and usage behaviours. Notably, the
features may enhance user satisfaction and engagement. This research underscores the importance of
understanding user interaction patterns to improve chatbot functionality and efficacy in delivering mental
health support.
MacNeill et al.
[23]
examined the effectiveness of a mental health chatbot, Wysa, a commercially available
conversational agent for public use via Website for Wysa,
[24]
for individuals with chronic diseases, specifically
arthritis and diabetes. In their randomized controlled trial involving 68 participants, those using the Wysa
chatbot formed the treatment group, while others served as the control group. The findings indicate that mental
health chatbots can provide effective support for individuals managing chronic conditions. Despite being cost-
effective and accessible, the study notes limitations in these programs, suggesting they may not be suitable for
everyone. This underscores the need for tailored approaches in digital mental health interventions to better
meet diverse user needs.
Suharwardy et al.
[25]
conducted a randomized controlled trial to assess the feasibility and impact of the Woebot
app, another non-commercial smartphone application, available on Android and iOS, on postpartum mental
health among women. Out of 192 participants, 68 utilized the chatbot for mental health assessment during their
postpartum period. The findings indicated that there was not a significant change in mental health outcomes
for those who used the app compared to those who did not. The study concluded that while the use of the
chatbot was acceptable among women in the early postpartum period, the lack of positive screening for
symptoms
In the book chapter, Negi
[26]
explores various AI-
health, highlighting tools such as Moodfit. Moodfit is a mobile application that delivers a personalized mental
wellness program, encompassing mindfulness exercises, mood tracking, and goal-setting features. Accessible
via get moodfit.com and available on both iOS and Android platforms, Moodfit offers a range of activities,
including breathing exercises and Cognitive Behavioral Therapy (CBT) thought records. Users can also maintain
a mood journal and receive reminders, with data visualizations like scatterplots to track mood, nutrition, and
other variables over time. Additionally, the app includes positive quotes and educational resources aimed at
fostering a positive mindset. Its user-friendly interface contributes to a satisfying overall user experience,
making it a valuable tool for many. Although it is available for public use, no research publications for Moodfit
were found that provide evidence on the evaluation metric and the usability of the chatbot.
In conclusion it is noted that only a limited number of chatbots have been developed within the timeframe of
this review, with very few undergoing public trials such as randomized controlled trials (RCTs). The ones that
are currently available for public use include ChatPal, Happify, Moodfit, Wysa, and Woebot. Each of these chatbot
has shown promise in addressing mental health needs, leveraging evidence-based techniques to provide
support and engage users effectively. ChatPal, for instance, targets the mental health and well-being of rural
populations, offering tailored interventions. Happify employs evidence-based activities and games to improve
emotional health. Moodfit focuses on tracking moods and providing tools for mental wellness. Wysa and Woebot
integrate cognitive-behavioral therapy principles to help users manage anxiety and depression through
conversational interactions. In conclusion, while there is a growing number of chatbots developed for mental
health support, the evidence of their efficacy is still emerging. The limited public trials and the variability in
their availability underscore the need for further research and evaluation. As these technologies evolve, ongoing
assessment will be crucial to ensure they effectively meet the needs of users and contribute positively to mental
health interventions.
4.2 Chatbot in general health
et al
et al.
et al.
et al
et al.
Görtz et al. study.
et al
5. Role of generative AI in healthcare: potential and limitations
Although large language models (LLMs) are not the primary focus of this review, their growing popularity,
coupled with the increasing research in this area, warrants brief attention. While the exploration of LLMs in
healthcare is not yet extensive, we have tried to highlight their potential applications and provide an overview
of their use in healthcare settings. The integration of generative AI, particularly large language models (LLMs)
like ChatGPT, into healthcare has garnered increasing interest, with studies evaluating their performance across
diverse clinical scenarios. Research shows that while AI systems can provide valuable support, they face
limitations in accuracy and reliability, particularly in complex, high-stakes medical conditions. For instance,
studies in emergency medicine,
[41]
ophthalmology,
[42]
and voice treatment decision-making. Dronkers et al.
[43]
However,
advancements in AI for domain-specific applications, such as gastrointestinal imaging, offer promise. A recent
study by Rau et al.
[44]
demonstrated that a gastrointestinal imaging-aware chatbot (GIA-CB), powered by GPT-4
and enhanced via the Llama Index framework, significantly outperformed a generic GPT-4 chatbot in providing
differential diagnoses based on imaging descriptions. The GIA-CB delivered accurate primary and differential
diagnoses in 90% of cases, with formulated rationale and source references, emphasizing the importance of
knowledge retrieval for explainable and trustworthy AI in clinical decision-making. The findings from Goodman
et al. work
[45]
align with the growing body of research evaluating the accuracy and reliability of AI systems in
healthcare. In their study, GPT-based chatbots (GPT-3.5 and GPT-4) were tested for accuracy and completeness
in answering 284 medical queries across 17 specialties, with promising results. Median accuracy and
completeness scores were high, especially for GPT-4, which demonstrated significant improvements over GPT-
3.5 and over time upon re-evaluation. While easy questions received near-perfect scores, harder queries
showed more variability, highlighting areas where AI systems still require refinement. The results reinforce the
notion that while AI has potential to augment clinical decision-making, its limitations, particularly in nuanced
or complex scenarios, necessitate ongoing validation and oversight. Parikh et al. study highlights the variability
and inaccuracies in AI chatbot-generated recommendations for oculoplastic surgeons.
[46]
Among 539 suggested
physicians, only 64.1% were oculoplastic specialists, with 38% being either non-existent or not practicing in
the specified city. Gender representation was skewed, with only 27.7% of recommendations being female.
Prompt phrasing signif
surgeons and ENTs instead of oculoplastic specialists. These findings underscore the need to address biases
and inaccuracies in AI-driven healthcare recommendations, e
increases. Despite these advancements, challenges remain, as AI-generated recommendations can lack depth
or consistency with guidelines, as seen in comparisons with established resources like UpToDate.
[47]
Nevertheless, AI tools demonstrate strong potential to enhance healthcare access, particularly in resource-
limited settings
[48]
and increase accessibility for stigmatizing health concerns.
[49]
Future developments will
require rigorous validation, continuous learning, and collaboration between developers and healthcare
professionals to refine accuracy, mitigate biases, and optimize patient outcomes.
6. Discussion
As Friedman
[50]
pointed out in his article on Clark Stanley's snake oil scam remedy, the authors point to the
similarly opaque, 'black box' character of sophisticated machine learning algorithms, which tend to defy
simplistic interpretation because of their intricate parameters. Given the very delicate nature of the health
domain, proper caution must be taken to ensure no casualties are caused as an effect of using such
technologically advanced interventions. Moreover, issues like data security and protection, and other safety
ethics must be prioritized for any of such interventions. Advancements in large language models have led to the
growing use of chatbot in various fields, including healthcare. One example is the Simsimi chatbot, which uses
an AI engine for communication but is not specifically tailored for domains like mental health. Chin et al. study
explores the potential of using this chatbot for mental health support by analysing user conversations that
[51]
While the chatbot demonstrates promise, it does not yet exhibit the
level of intelligence needed for such sensitive topics. Notably, users of chatbots like Simsimi do not typically
seek social support for life difficulties as they might on social media, yet they expect features like active listening
skills and a safe space to express emotions like sadness or depression. The study analysing 152,783
conversations from both Western (Canada, the UK, the US) and Eastern countries (Indonesia, India, Malaysia,
the Philippines, and Thailand) found that Eastern users were more likely to use words related to sadness, while
Western users discussed more vulnerable topics like mental health and sensitive issues such as death and swear
words. While these findings point to the potential of chatbots in the mental health space, they also highlight the
need for professional intervention and robust privacy protections. These studies reflect a broader trend in the
research on chatbot across various domains, where user preferences and the adaptability of technology remain
central to its effectiveness.
Several other studies underscore the importance of tailoring chatbot to specific user needs while addressing
challenges like emotional engagement and privacy.
[52]
For example, investigates the use of chatbots in delivering
genetic services, emphasizing that while patients are generally receptive to chatbots for some aspects of genetic
lex issues arise. Similarly, Park
et al. examined how emotional disclosure through AI chatbots impacts user satisfaction, finding that chatbots
that facilitate emotional expression were perceived as more engaging.
[53]
However, the lack of publicly available
information on these tools limits the generalizability of the results. Bowman et al. also explored how the tone
of chatbot interactions, specifically politeness, influences user experiences in mood logging for mental health.
[54]
They found that while politeness could enhance supportive interactions, it could also lead to feelings of distrust
or condescension if not carefully balanced. Other studies, such as Schmitz and Becker highlight the utility of
chatbots in niche areas like dementia caregiving,
[55]
where personalized information and user engagement are
key to their success. While some chatbots, such as PharmindBot
[56]
have proven effective for educational and
data collection purposes in healthcare, their impact on patient care remains unclear. Trials such as Al-Hilli et
al.
[57]
randomized study on genetic counselling chatbots suggest that AI can match traditional counselling in
certain contexts, but further research is needed to ensure broader applicability. Finally, emerging technologies,
such as mindfulness-based stress reduction via chatbots and virtual humans, also show potential. While virtual
humans demonstrated better engagement, chatbots lagged in adherence to tasks, underscoring the need for
continued development to enhance the perceived empathy and effectiveness of these tools in diverse healthcare
settings. Together, these studies suggest that while chatbot have significant promise in healthcare, their
development must carefully consider user preferences, emotional needs, and the complexities of sensitive
issues like mental health, ensuring that these tools are both effective and ethically sound. Mobile applications,
particularly chatbot-based platforms like Woebot, Wysa, and Anna, are gaining popularity for providing mental
health support,
[58]
with Anna being developed by Happify Health to offer innovative digital mental health care.
[59]
Anna, available on happify.com and the Play Store, features a user-friendly interface with various activities like
guided meditations, quizzes, and reflective prompts aimed at fostering mindfulness. However, despite its
accessibility and ease of use, the
replace traditional therapy. This trend highlights the growing popularity of mobile mental health tools, but also
underscores the need for appropriate security measures, especially considering the risk of young children
accessing such apps. To ensure user privacy and data protection, implementing features like numeric lock or
other forms of authentication is strongly recommended.
7. Conclusion
In conclusion, the increasing interest in utilizing chatbots for mental and general health care management is
evident, with several publicly available chatbots for mental health support, such as Chatpal, Wysa, and Woebot.
Numerous publications highlight the ongoing efforts to improve these systems, particularly in the mental health
space, where randomized controlled trials (RCTs) have demonstrated their potential for broader public use,
provided they undergo necessary advancements and refinements. However, in the domain of physical health
care, we found no chatbots that are ready for public use, with most still in user acceptance or beta testing phases.
This underscores a significant gap in the development and implementation of chatbots for physical health care,
highlighting the need for further innovation and progress in this area.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
No data were generated or analyzed during the current study. Therefore, data sharing is not applicable to this
article.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
Supporting Information
Not applicable.
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