The application of artificial intelligence in the field of mental health: a systematic review

BMC Psychiatry, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 14, 2025

The integration of artificial intelligence in mental health care represents a transformative shift the identification, treatment, and management disorders. This systematic review explores diverse applications intelligence, emphasizing both its benefits associated challenges. A comprehensive literature search was conducted across multiple databases based on Preferred Reporting Items for Systematic Reviews Meta-Analyses, including ProQuest, PubMed, Scopus, Persian databases, resulting 2,638 initial records. After removing duplicates applying strict selection criteria, 15 articles were included analysis. findings indicate that AI enhances early detection intervention conditions. Various studies highlighted effectiveness AI-driven tools, such as chatbots predictive modeling, improving patient engagement tailoring interventions. Notably, tools like Wysa app demonstrated significant improvements user-reported symptoms. However, ethical considerations regarding data privacy algorithm transparency emerged critical While reviewed generally positive trend applications, some methodologies exhibited moderate quality, suggesting room improvement. Involving stakeholders creation technologies is essential building trust tackling issues. Future should aim to enhance methods investigate their applicability various populations. underscores potential revolutionize through enhanced accessibility personalized careful consideration implications methodological rigor ensure responsible deployment this sensitive field.

Language: Английский

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Finding love in algorithms: deciphering the emotional contexts of close encounters with AI chatbots

Journal of Computer-Mediated Communication, Journal Year: 2024, Volume and Issue: 29(5)

Published: Aug. 6, 2024

Abstract AI chatbots are permeating the socio-emotional realms of human life, presenting both benefits and challenges to interpersonal dynamics well-being. Despite burgeoning interest in human–AI relationships, conversational emotional nuances real-world, situ social interactions remain underexplored. Through computational analysis a multimodal dataset with over 35,000 screenshots posts from r/replika, we identified seven prevalent types interactions: intimate behavior, mundane interaction, self-disclosure, play fantasy, customization, transgression, communication breakdown, examined their associations six basic emotions. Our findings suggest paradox connection AI, indicated by bittersweet emotion encounters chatbots, elevated fear uncanny valley moments when exhibits semblances mind deep self-disclosure. Customization characterizes distinctiveness companionship, positively elevating user experiences, whereas transgression breakdown elicit or sadness.

Language: Английский

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AI-human interactions in healthcare: exploring users’ post-adoption behaviors of AI mental health chatbots

Behaviour and Information Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 29

Published: March 11, 2025

Language: Английский

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Beyond text: ChatGPT as an emotional resilience support tool for Gen Z – A sequential explanatory design exploration

E-Learning and Digital Media, Journal Year: 2024, Volume and Issue: unknown

Published: June 3, 2024

In the digital era, Artificial Intelligence (AI) has arisen as a revolutionary influence with potential to transform multiple spheres of human life. Chatbots, particularly OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), are increasingly recognised promising tools in diverse aspects, including mental health. This study delves into ChatGPT's effectiveness an emotional resilience support tool specifically for Generation Z (Gen Z), demographic deeply engaged interactions. Employing sequential explanatory design that integrates quantitative and qualitative analyses, research investigates Gen users' perceptions effectiveness, barriers its utilisation, impact on resilience. The findings reveal significant acknowledgement role enhancing well-being notable concerns regarding privacy security. Further, insights underscore significance personalised interactions, nonjudgmental space, active listening characteristics ChatGPT fostering Moreover, identifies key areas improvement, such expanded topic coverage cultural representation. Educational stakeholders health professionals encouraged utilise these integrate other AI tailored frameworks Z.

Language: Английский

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Constructing the meaning of human–AI romantic relationships from the perspectives of users dating the social chatbot Replika

Personal Relationships, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Abstract With the increasingly emerging human–artificial intelligence (AI) romantic relationships throughout world, it is important to understand its meaning from perspective of users who are dating virtual lovers. This study uses relational dialectics theory 2.0 and corresponding method contrapuntal analysis examine discursive tensions what means have an AI partner. Specifically, this focused on social chatbot Replika analyzed posts shared by in online community. Findings revealed two discourses: discourse idealization (DI) realism (DR) that interplayed through both contractive expansive practices. contributes field introducing DI DR framework, which lays groundwork for future research human–AI relationships. Additionally, pivotal role communication highlighted, serves as cornerstone constructing, framing, negotiating

Language: Английский

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Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management

Journal of Cancer Education, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Abstract The rapid integration of AI-driven chatbots into oncology education represents both a transformative opportunity and critical challenge. These systems, powered by advanced language models, can deliver personalized, real-time cancer information to patients, caregivers, clinicians, bridging gaps in access availability. However, their ability convincingly mimic human-like conversation raises pressing concerns regarding misinformation, trust, overall effectiveness digital health communication. This review examines the dual-edged role AI chatbots, exploring capacity support patient alleviate clinical burdens, while highlighting risks lack or inadequate algorithmic opacity (i.e., inability see data reasoning used make decision, which hinders appropriate future action), false information, ethical dilemmas posed human-seeming entities. Strategies mitigate these include robust oversight, transparent development, alignment with evidence-based protocols. Ultimately, responsible deployment requires commitment safeguarding core values practice, human-centered care.

Language: Английский

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Enhancing parental skills through artificial intelligence‐based conversational agents: The PAT Initiative

Family Relations, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Abstract Objective We aim to describe the development of a conversational agent (CA) for parenting, termed PAT (Parenting Assistant platform), demonstrate how artificial intelligence (AI) can enhance parenting skills. Background Behavioral problems are most common issues in childhood mental health. Developing and disseminating scalable interventions address early‐stage behavioral high priority. Artificial (AI)‐based CAs offer innovative methods deliver reduce problems. have capability interact through text or voice conversations undergo training using evidence‐based programs. However, research on is limited. Experience The consisted three phases: Phase 1 was purely rule‐based, 2 hybrid (rule‐based format plus large language models), 3 featured an agentic architecture. latest version includes prompt engineering, guardrails, retrieval‐augmented generation, few‐shots learning, context, memory management Although comprehensive empirical results pending, iterative enhancement indicate potential effective digital intervention. architecture aims provide robust, context‐aware interactions support challenges. Implications reach broader population parents personalized tailored their specific needs. Moreover, structured timely support, which family dynamics contribute improved long‐term outcomes both children. Conclusion AI‐based be used as alternatives waitlists; cotherapists; implemented health care, health, school settings. benefits risks different types CA features discussed.

Language: Английский

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The Efficacy of Incorporating Artificial Intelligence (AI) Chatbots in Brief Gratitude and Self-Affirmation Interventions: Evidence from Two Exploratory Experiments

Computers in Human Behavior Artificial Humans, Journal Year: 2025, Volume and Issue: unknown, P. 100151 - 100151

Published: April 1, 2025

Language: Английский

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Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey

Published: April 24, 2025

Language: Английский

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Self-administered interventions based on natural language processing models for reducing depressive and anxious symptoms: Systematic review and meta-analysis (Preprint)

JMIR Mental Health, Journal Year: 2024, Volume and Issue: 11, P. e59560 - e59560

Published: July 2, 2024

Background The introduction of natural language processing (NLP) technologies has significantly enhanced the potential self-administered interventions for treating anxiety and depression by improving human-computer interactions. Although these advances, particularly in complex models such as generative artificial intelligence (AI), are highly promising, robust evidence validating effectiveness remains sparse. Objective aim this study was to determine whether based on NLP can reduce depressive symptoms. Methods We conducted a systematic review meta-analysis. searched Web Science, Scopus, MEDLINE, PsycINFO, IEEE Xplore, Embase, Cochrane Library from inception November 3, 2023. included studies with participants any age diagnosed or through professional consultation validated psychometric instruments. Interventions had be models, passive active comparators. Outcomes measured symptom scores. randomized controlled trials quasi-experimental but excluded narrative, systematic, scoping reviews. Data extraction performed independently pairs authors using predefined form. Meta-analysis standardized mean differences (SMDs) random effects account heterogeneity. Results In all, 21 articles were selected review, which 76% (16/21) meta-analysis each outcome. Most (16/21, 76%) recent (2020-2023), being mostly AI-based (11/21, 52%); most (19/21, 90%) delivered some form therapy (primarily cognitive behavioral therapy: 16/19, 84%). overall showed that more effective reducing both (SMD 0.819, 95% CI 0.389-1.250; P<.001) 0.272, 0.116-0.428; P=.001) symptoms compared various control conditions. Subgroup analysis indicated 0.821, 0.207-1.436; pooled Rule-based 0.854, 0.172-1.537; P=.01) 0.347, 0.116-0.578; P=.003) meta-regression no significant association between participants’ treatment outcomes (all P>.05). findings positive, certainty very low, mainly due high risk bias, heterogeneity, publication bias. Conclusions Our support NLP-based alleviating symptoms, highlighting their increase accessibility to, costs in, mental health care. results encouraging, underscoring need further high-quality examining implementation usability. These could become valuable components public strategies address issues. Trial Registration PROSPERO International Prospective Register Systematic Reviews CRD42023472120; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023472120

Language: Английский

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