Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Healthcare
Special Issues
Artificial Intelligence Chatbots and Mental Health
share announcement

Artificial Intelligence Chatbots and Mental Health

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 8719

Editor

Dr. Olympia Konstantakopoulou

E-Mail Website
Guest Editor
Clinical Epidemiology Laboratory, Faculty of Nursing, National and Kapodistrian University of Athens, 11527 Athens, Greece
Interests: research methodology; artificial intelligence; economic evaluation; mental health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence chatbots are emerging as valuable tools in mental healthcare, offering scalable and immediate support for patients between clinical visits. These systems can assist with screening for anxiety or depression, delivering evidence-based interventions such as cognitive behavioral therapy exercises and monitoring patient-reported outcomes in real time. For clinicians, chatbots can help bridge gaps in care, reduce workload and provide early alerts for potential crises. However, their integration into practice requires careful consideration of ethical and clinical standards, including data security, patient consent and ensuring that automated responses complement—not replace—professional judgment. When implemented responsibly, Artificial Intelligence chatbots can enhance continuity of care and improve patient engagement in mental health management.

This Special Issue aims to explore the evolving role of Artificial Intelligence chatbots in the domain of mental healthcare, focusing on their potential to enhance accessibility, early intervention and patient engagement. It seeks to bring together interdisciplinary research and clinical insights that examine the design, implementation, and ethical considerations of Artificial Intelligence-driven conversational agents in mental health settings. In this context, it is critical to assess people’s attitudes towards Artificial Intelligence mental health chatbots with valid scales to ensure appropriate measurements and interventions. By highlighting both the opportunities and limitations of these technologies, the Issue will provide a critical platform for healthcare professionals, technologists and policymakers to assess how Artificial Intelligence chatbots can be responsibly integrated into mental health services to support diverse populations and improve outcomes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Evaluation of chatbot interventions for anxiety, depression, etc.
  • Comparative studies between chatbot-based and traditional therapies.
  • Development and validation of scales to measure attitudes towards Artificial Intelligence chatbots for mental health support.
  • Artificial Intelligence chatbots for mental and emotional health support.
  • Integration of Artificial Intelligence chatbots into mental healthcare pathways.
  • Ethical, legal, and privacy considerations regarding the use of Artificial Intelligence chatbots.
  • Data protection and patient confidentiality.
  • Informed consent and transparency in Artificial Intelligence interactions.
  • Limitations and safeguards in high-risk mental health issues.
  • Policy frameworks for safe deployment in healthcare systems.
  • Educational and training implications for healthcare professionals.

I look forward to receiving your contributions.

Dr. Olympia Konstantakopoulou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • artificial intelligence
  • chatbots
  • mental health
  • attitudes
  • education
  • interventions
  • scales
  • measurement
  • ethical issues

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 1939 KB  
Article
Assessment of Perceived Facial Age Changes Following Orthognathic Surgery Using Artificial Intelligence
by Özlem Elverişli, Hilal Alan, Ümit Yolcu and Ayşegül Evren
Healthcare 2026, 14(14), 2200; https://doi.org/10.3390/healthcare14142200 - 21 Jul 2026
Viewed by 270
Abstract
Background/Objectives: Orthognathic surgery is performed to improve facial esthetics and function in patients with dentofacial deformities. This study aimed to evaluate the impact of orthognathic surgery on perceived facial age using an artificial intelligence (AI)-based age-estimation tool and to examine accompanying changes in [...] Read more.
Background/Objectives: Orthognathic surgery is performed to improve facial esthetics and function in patients with dentofacial deformities. This study aimed to evaluate the impact of orthognathic surgery on perceived facial age using an artificial intelligence (AI)-based age-estimation tool and to examine accompanying changes in self-rated flourishing and social appearance anxiety. Methods: This retrospective, single-arm, before-and-after study included 27 patients who underwent bilateral sagittal split ramus osteotomy (BSSRO) and/or Le Fort I osteotomy. Preoperative and postoperative standardized frontal photographs were analyzed with the AI-based application “AgeBot: How Old Do I Look?”. Participants also completed the Social Appearance Anxiety Scale (SAAS) and the Flourishing Scale (FS; Turkish adaptation by Telef). Both questionnaires were completed at a single postoperative interview—first for the remembered preoperative state and then for the current postoperative state—so the psychosocial pre–post comparisons reflect retrospectively perceived change. Normality was assessed on the paired differences; the primary AI-age outcome was analyzed with the paired t-test (confirmed by the Wilcoxon signed-rank test) and the psychosocial outcomes with the Wilcoxon signed-rank test, with effect sizes reported. A data-informed sensitivity analysis, rather than an a priori power calculation, accompanied the primary outcome. Results: AI-estimated facial age did not change significantly after surgery (mean difference [preoperative−postoperative] 0.11 ± 2.49 years, 95% CI −0.87 to 1.09, p = 0.818; Cohen’s dz = 0.04), and the confidence interval excluded the previously reported 1.31-year reduction. Exploratory change-score comparisons showed no significant differences by sex or smoking status. Postoperative SAAS scores were lower than retrospective preoperative scores (median 37 [IQR 24.5–42.0] vs. 51 [43.0–56.0]; Z = −4.07, p < 0.001, r = 0.55), and postoperative FS scores were higher (median 46 [40.0–51.5] vs. 45 [38.5–45.0]; Z = −2.45, p = 0.014, r = 0.37). Conclusions: Orthognathic surgery was not associated with a statistically significant change in AI-estimated facial age in this small cohort. More favorable social appearance anxiety and flourishing scores were observed following treatment, but the retrospective, uncontrolled assessment precludes causal attribution. AI-assisted facial age estimation may complement patient-reported outcomes, but validated, repeatability-tested tools are required before it can serve as a primary outcome measure. Full article
(This article belongs to the Special Issue Artificial Intelligence Chatbots and Mental Health)
►▼ Show Figures

Figure 1

20 pages, 527 KB  
Article
AI Versus Human-Delivered Online Cognitive Behavioral Therapy for Anxiety Symptoms in Young Adults: A Randomized Controlled Trial
by Weihao Huang, Yiyang Wu, Yujin Shen, Haoran Song, Chen Ye, Ruoyu Lin, You Wang and Xueling Yang
Healthcare 2026, 14(10), 1325; https://doi.org/10.3390/healthcare14101325 - 13 May 2026
Viewed by 864
Abstract
Objective: This study aimed to compare the effectiveness of online cognitive behavioral therapy (CBT) delivered by an AI chatbot versus human peer counselors (participants were told it was AI) in reducing anxiety symptoms in young adults. Methods: Ninety young adults with mild-to-severe anxiety [...] Read more.
Objective: This study aimed to compare the effectiveness of online cognitive behavioral therapy (CBT) delivered by an AI chatbot versus human peer counselors (participants were told it was AI) in reducing anxiety symptoms in young adults. Methods: Ninety young adults with mild-to-severe anxiety were randomized to a 4-week intervention of AI-CBT (n = 30), peer-counselor-CBT (n = 30), or a no-intervention control (n = 30). The primary outcome, anxiety, was assessed at baseline, mid-point, and post-intervention. Secondary outcomes (the self-efficacy for exercise, sleep quality), psychotherapy benefit, and qualitative user experiences were also evaluated. Results: Both AI and human-delivered interventions led to significant within-group reductions in anxiety (p < 0.05). However, in the primary intention-to-treat analysis, neither intervention demonstrated a statistically significant advantage over the no-intervention control group at post-intervention. A secondary per-protocol analysis suggested a benefit for the human-delivered intervention among study completers. Notably, participants in the AI group reported significantly lower perceived treatment benefit than the human group (p < 0.001). Qualitative analyses indicated that while AI was valued for accessibility and consistency, human intervention was perceived as more flexible in guidance, individualized, emotionally supportive, and conducive to deeper exploration. Conclusions: In this exploratory trial, both AI- and peer-counselor-CBT showed within-group promise, but the evidence does not support their efficacy over a no-intervention control. The AI’s limitations in providing flexible, emotionally supportive, and personalized interaction likely explain the efficacy gap observed between the two interventions. While AI may serve as a scalable support tool, claims of clinical efficacy require significant caution. These preliminary findings warrant replication in a prospectively registered confirmatory trial. Full article
(This article belongs to the Special Issue Artificial Intelligence Chatbots and Mental Health)
►▼ Show Figures

Figure 1

23 pages, 1023 KB  
Article
Development and Validation of the Artificial Intelligence in Mental Health Scale: Application for AI Mental Health Chatbots
by Aglaia Katsiroumpa, Olympia Konstantakopoulou, Ioannis Moisoglou, Parisis Gallos, Olga Galani, Paschalina Lialiou, Maria Tsiachri and Petros Galanis
Healthcare 2025, 13(24), 3269; https://doi.org/10.3390/healthcare13243269 - 12 Dec 2025
Cited by 3 | Viewed by 4692
Abstract
Background/Objectives: Artificial intelligence (AI)-based chatbots present a viable approach to overcoming several challenges associated with conventional psychotherapy, such as high financial costs, limited access to mental health professionals, and geographical or logistical barriers. Thus, these chatbots are increasingly employed as complementary tools [...] Read more.
Background/Objectives: Artificial intelligence (AI)-based chatbots present a viable approach to overcoming several challenges associated with conventional psychotherapy, such as high financial costs, limited access to mental health professionals, and geographical or logistical barriers. Thus, these chatbots are increasingly employed as complementary tools to traditional therapeutic practices in mental health care. Our aim was to develop and validate a scale to measure attitudes toward the use of AI-based chatbots for mental health support, i.e., the Artificial Intelligence in Mental Health Scale (AIMHS). Methods: A multidisciplinary panel of experts assessed the content validity. To confirm face validity, we carried out cognitive interviews and calculated the item-level face validity index. We applied factor analysis to verify the construct structure. We assessed measurement invariance across demographic subgroups. Concurrent validity was evaluated using three valid instruments. Reliability was tested through Cronbach’s alpha, Cohen’s kappa, and the intraclass correlation coefficient. Results: Factor analysis supported a two-factor five-item model. The two factors were technical and personal advantages, and explained 81.28% of the variance. The AIMHS demonstrated adequate concurrent validity, evidenced by statistically significant correlations with Artificial Intelligence Attitude Scale (r = 0.405, p-value < 0.001), Attitudes Towards Artificial Intelligence Scale (acceptance subscale; r = 0.401, p-value < 0.001, fear subscale; r = −0.151, p-value = 0.002), and Short Trust in Automation Scale (r = 0.450, p-value < 0.001). Configural, metric and scalar invariance were supported by our findings. Cronbach’s alpha was 0.798, and intraclass correlation coefficient was 0.938. Cohen’s kappa for the five items ranged from 0.760 to 0.848. Conclusions: The AIMHS is a five-item psychometrically sound and user-friendly instrument capturing two dimensions; technical and personal advantages. Future research should be undertaken to further evaluate the psychometric properties of the AIMHS across diverse populations and contexts. Full article
(This article belongs to the Special Issue Artificial Intelligence Chatbots and Mental Health)
►▼ Show Figures

Figure 1

Review

Jump to: Research

21 pages, 1719 KB  
Review
From Tool to Agent: A Semi-Systematic Review of Human–AI Alignment and a Proposed Tiered Healing Ecosystem for Mental Health
by Anran Ma, Jingying Chen and Zhiyi Yang
Healthcare 2026, 14(6), 820; https://doi.org/10.3390/healthcare14060820 - 23 Mar 2026
Viewed by 1973
Abstract
Background: This study aims to systematically analyze the structural transition of AI in mental health, differentiating between passive tools and autonomous agents, and to propose a governance framework to facilitate responsible integration or mitigate integration risks. Methods: Employing a semi-systematic approach, [...] Read more.
Background: This study aims to systematically analyze the structural transition of AI in mental health, differentiating between passive tools and autonomous agents, and to propose a governance framework to facilitate responsible integration or mitigate integration risks. Methods: Employing a semi-systematic approach, we screened records from IEEE Xplore, PubMed, and ACM DL, ultimately analyzing 61 included studies. We track the transition from the first paradigm, AI-as-Tool (AI-T) to the second paradigm, AI-as-Agent (AI-A). Results: Early empirical evidence suggests that AI-A systems may assist in fostering preliminary working alliances and demonstrate potential for symptom reduction in controlled settings; however, their efficacy cannot currently be equated with, nor serve as a replacement for, standard low-intensity clinical care. Conclusions: To mitigate these risks, we propose the Tiered Human–AI Healing Ecosystem (THHE) for mental health. This framework utilizes dynamic autonomy modulation—automatically restricting AI agency based on real-time risk markers—to manage transitions between AI-led support and human-led care, promoting clinical safety. Full article
(This article belongs to the Special Issue Artificial Intelligence Chatbots and Mental Health)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop