The Role of Artificial Intelligence in Managing Bipolar Disorder: A New Frontier in Patient Care DOI Open Access
Jelena Milić,

Iva Zrnic,

Edita Grego

и другие.

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(7), С. 2515 - 2515

Опубликована: Апрель 7, 2025

Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients healthcare providers. Traditional treatment methods, including medication therapy, remain vital, but there increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has potential improve mood episode prediction, personalize plans, provide real-time support, offering new opportunities managing more effectively. Our primary objective was explore role transforming management BD, specifically tracking, personalized regimens. Methods: To management, we conducted review recent literature using key search terms. We included studies discussed applications personalization. The were selected based on their relevance AI's with attention PICO criteria: Population-individuals diagnosed BD; Intervention-AI tools personalization, support; Comparison-traditional methods (when available); Outcome-measures effectiveness, improvements patient care. Results: findings from research reveal promising developments use Studies suggest AI-powered can enable proactive care, improving outcomes reducing burden professionals. ability analyze data wearable devices, smartphones, even social media platforms provides valuable insights early detection dynamic adjustments. Conclusions: While still its stages, it presents transformative However, further development are crucial fully realize supporting optimizing efficacy.

Язык: Английский

The application of artificial intelligence in the field of mental health: a systematic review DOI Creative Commons
Raziye Dehbozorgi, Sanaz Zangeneh,

Elham Khooshab

и другие.

BMC Psychiatry, Год журнала: 2025, Номер 25(1)

Опубликована: Фев. 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.

Язык: Английский

Процитировано

4
Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management DOI Creative Commons
Aaron Lawson McLean,

Vagelis Hristidis

Journal of Cancer Education, Год журнала: 2025, Номер unknown

Опубликована: Фев. 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.

Язык: Английский

Процитировано

1
Enhancing parental skills through artificial intelligence‐based conversational agents: The PAT Initiative DOI Creative Commons
Milagros Escoredo, Karin Mostovoy,

Ross Schickler

и другие.

Family Relations, Год журнала: 2025, Номер unknown

Опубликована: Фев. 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.

Язык: Английский

Процитировано

1
Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey DOI
Jacy Reese Anthis, Janet V. T. Pauketat, Ali Ladak

и другие.

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

1
Finding love in algorithms: deciphering the emotional contexts of close encounters with AI chatbots DOI Creative Commons
Han Li, Renwen Zhang

Journal of Computer-Mediated Communication, Год журнала: 2024, Номер 29(5)

Опубликована: Авг. 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.

Язык: Английский

Процитировано

9
Constructing the meaning of human–AI romantic relationships from the perspectives of users dating the social chatbot Replika DOI
Shuyi Pan, Yi Mou

Personal Relationships, Год журнала: 2024, Номер unknown

Опубликована: Сен. 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

Язык: Английский

Процитировано

8
AI-human interactions in healthcare: exploring users’ post-adoption behaviors of AI mental health chatbots DOI
Pouyan Esmaeilzadeh, Khaled Hassanein, Milena Head

и другие.

Behaviour and Information Technology, Год журнала: 2025, Номер unknown, С. 1 - 29

Опубликована: Март 11, 2025

Язык: Английский

Процитировано

1
Beyond text: ChatGPT as an emotional resilience support tool for Gen Z – A sequential explanatory design exploration DOI

K. Kavitha,

V. P. Joshith,

Sonal Sharma

и другие.

E-Learning and Digital Media, Год журнала: 2024, Номер unknown

Опубликована: Июнь 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.

Язык: Английский

Процитировано

6
Self-administered interventions based on natural language processing models for reducing depressive and anxious symptoms: Systematic review and meta-analysis (Preprint) DOI Creative Commons
David Villarreal‐Zegarra, C. Mahony Reátegui-Rivera, Jackeline García-Serna

и другие.

JMIR Mental Health, Год журнала: 2024, Номер 11, С. e59560 - e59560

Опубликована: Июль 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

Язык: Английский

Процитировано

5
Evaluating the Clinical Validity and Reliability of Artificial Intelligence-Enabled Diagnostic Tools in Neuropsychiatric Disorders DOI Open Access

Satneet Singh,

Jade Gambill,

Mary Attalla

и другие.

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Окт. 16, 2024

Neuropsychiatric disorders (NPDs) pose a substantial burden on the healthcare system. The major challenge in diagnosing NPDs is subjective assessment by physician which can lead to inaccurate and delayed diagnosis. Recent studies have depicted that integration of artificial intelligence (AI) neuropsychiatry could potentially revolutionize field precisely complex neurological mental health timely fashion providing individualized management strategies. In this narrative review, authors examined current status AI tools assessing neuropsychiatric evaluated their validity reliability existing literature. analysis various datasets including MRI scans, EEG, facial expressions, social media posts, texts, laboratory samples accurate diagnosis conditions using machine learning has been profoundly explored article. recent trials tribulations encouraging future scope utility application discussed. Overall proved be feasible applicable it about time research translates clinical settings for favorable patient outcomes. Future should focus presenting higher quality evidence superior adaptability establish guidelines providers maintain standards.

Язык: Английский

Процитировано

5