Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact

Social Sciences, Год журнала: 2024, Номер 13(7), С. 381 - 381

Опубликована: Июль 22, 2024

AI has the potential to revolutionize mental health services by providing personalized support and improving accessibility. However, it is crucial address ethical concerns ensure responsible beneficial outcomes for individuals. This systematic review examines considerations surrounding implementation impact of artificial intelligence (AI) interventions in field well-being. To a comprehensive analysis, we employed structured search strategy across top academic databases, including PubMed, PsycINFO, Web Science, Scopus. The scope encompassed articles published from 2014 2024, resulting 51 relevant articles. identifies 18 key considerations, 6 associated with using wellbeing (privacy confidentiality, informed consent, bias fairness, transparency accountability, autonomy human agency, safety efficacy); 5 principles development technologies settings practice positive (ethical framework, stakeholder engagement, review, mitigation, continuous evaluation improvement); 7 practices, guidelines, recommendations promoting use (adhere transparency, prioritize data privacy security, mitigate involve stakeholders, conduct regular reviews, monitor evaluate outcomes). highlights importance By addressing privacy, bias, oversight, evaluation, can that like chatbots AI-enabled medical devices are developed deployed an ethically sound manner, respecting individual rights, maximizing benefits while minimizing harm.

Язык: Английский

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The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems

BMC Psychology, Год журнала: 2025, Номер 13(1)

Опубликована: Фев. 28, 2025

The increasing demand for psychotherapy and limited access to specialists underscore the potential of artificial intelligence (AI) in mental health care. This study evaluates effectiveness AI-powered Friend chatbot providing psychological support during crisis situations, compared traditional psychotherapy. A randomized controlled trial was conducted with 104 women diagnosed anxiety disorders active war zones. Participants were randomly assigned two groups: experimental group used daily support, while control received 60-minute sessions three times a week. Anxiety levels assessed using Hamilton Rating Scale Beck Inventory. T-tests analyze results. Both groups showed significant reductions levels. receiving therapy had 45% reduction on scale 50% scale, 30% 35% group. While provided accessible, immediate proved more effective due emotional depth adaptability by human therapists. particularly beneficial settings where therapists limited, proving its value scalability availability. However, engagement notably lower in-person therapy. offers scalable, cost-effective solution situations may not be accessible. Although remains reducing anxiety, hybrid model combining AI interaction could optimize care, especially underserved areas or emergencies. Further research is needed improve AI's responsiveness adaptability.

Язык: Английский

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

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

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Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications

Psychological Medicine, Год журнала: 2025, Номер 55

Опубликована: Янв. 1, 2025

Abstract Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in domains diagnosis, monitoring, intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, Scopus) was conducted from inception February 2024, a total 85 relevant studies were included according preestablished inclusion criteria. The methods most frequently used support vector machine random forest for learning chatbot tools appeared be accurate detecting, classifying, predicting risk conditions as well treatment response monitoring ongoing prognosis disorders. Future directions should focus on developing more diverse robust datasets enhancing transparency interpretability models improve clinical practice.

Язык: Английский

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Evaluating Diagnostic Accuracy and Treatment Efficacy in Mental Health: A Comparative Analysis of Large Language Model Tools and Mental Health Professionals

European Journal of Investigation in Health Psychology and Education, Год журнала: 2025, Номер 15(1), С. 9 - 9

Опубликована: Янв. 18, 2025

Large language models (LLMs) offer promising possibilities in mental health, yet their ability to assess disorders and recommend treatments remains underexplored. This quantitative cross-sectional study evaluated four LLMs (Gemini 2.0 Flash Experimental), Claude (Claude 3.5 Sonnet), ChatGPT-3.5, ChatGPT-4) using text vignettes representing conditions such as depression, suicidal ideation, early chronic schizophrenia, social phobia, PTSD. Each model’s diagnostic accuracy, treatment recommendations, predicted outcomes were compared with norms established by health professionals. Findings indicated that for certain conditions, including depression PTSD, like ChatGPT-4 achieved higher accuracy human However, more complex cases, LLM performance varied, achieving only 55% while other professionals performed better. tended suggest a broader range of proactive treatments, whereas recommended targeted psychiatric consultations specific medications. In terms outcome predictions, generally optimistic regarding full recovery, especially treatment, lower recovery rates partial rates, particularly untreated cases. While range, conservative highlight the need professional oversight. provide valuable support diagnostics planning but cannot replace discretion.

Язык: Английский

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Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Сен. 16, 2024

Maternal health remains a critical global challenge, with disparities in access to care and quality of services contributing high maternal mortality morbidity rates. Artificial intelligence (AI) has emerged as promising tool for addressing these challenges by enhancing diagnostic accuracy, improving patient monitoring, expanding care. This review explores the transformative role AI healthcare, focusing on its applications early detection pregnancy complications, personalized care, remote monitoring through AI-driven technologies. tools such predictive analytics machine learning can help identify at-risk pregnancies guide timely interventions, reducing preventable neonatal complications. Additionally, AI-enabled telemedicine virtual assistants are bridging healthcare gaps, particularly underserved rural areas, accessibility women who might otherwise face barriers Despite potential benefits, data privacy, algorithmic bias, need human oversight must be carefully addressed. The also discusses future research directions, including globally ethical frameworks integration. holds revolutionize both accessibility, offering pathway safer, more equitable outcomes.

Язык: Английский

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Predictive Analytics in Clinical Psychology

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 313 - 332

Опубликована: Янв. 3, 2025

Predictive analytics, powered by advancements in machine learning (ML), is reshaping the landscape of clinical psychology and mental health care. This paper explores transformative potential ML algorithms early diagnosis, personalized treatment planning, predictive risk assessments for disorders. By analysing complex datasets, including behavioural, genetic, environmental variables, models provide unprecedented accuracy identifying patterns factors associated with conditions such as depression, anxiety, bipolar disorder, schizophrenia. The study highlights integration natural language processing (NLP) patient interactions, wearable technologies real-time monitoring, reinforcement adaptive therapeutic interventions. concludes emphasizing a collaborative approach involving clinicians, data scientists, policymakers to ensure equitable effective implementation.

Язык: Английский

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From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Biomedicines, Год журнала: 2025, Номер 13(1), С. 167 - 167

Опубликована: Янв. 12, 2025

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence mental health disorders like depression schizophrenia, which necessitate precise, innovative approaches. Emerging technologies artificial intelligence, induced pluripotent stem cells, multi-omics potential transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies animal models single-variable analyses continue be used, frequently failing capture complexities human conditions. Summary: This review critically evaluates transition serendipity precision-based in research. It focuses key innovations dynamic systems modeling network-based approaches that use genetic, molecular, environmental data identify new therapeutic targets. Furthermore, it emphasizes importance interdisciplinary collaboration human-specific overcoming limitations Conclusions: We highlight precision psychiatry’s transformative revolutionizing care. paradigm shift, combines cutting-edge systematic frameworks, promises increased diagnostic accuracy, reproducibility, efficiency, paving way tailored better patient outcomes

Язык: Английский

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Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach

AI, Год журнала: 2025, Номер 6(1), С. 14 - 14

Опубликована: Янв. 16, 2025

Background: AI-driven mental health solutions offer transformative potential for improving healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing alternatives based on key criteria, including feasibility of implementation, cost-effectiveness, scalability, ethical compliance, user satisfaction, impact clinical outcomes. Methods: A fuzzy multi-criteria decision-making (MCDM) model, consisting TOPSIS ARAS, was employed to rank alternatives, while hybridization two methods used address discrepancies between methods, each emphasizing distinct evaluative aspect. Results: Fuzzy TOPSIS, focusing closeness ideal solution, ranked personalization care (A5) as top alternative with coefficient 0.50, followed engagement (A2) at 0.45. which evaluates cumulative performance, also A5 highest, an overall performance rating Si = 0.90 utility degree Qi 0.92. Combining both provided balanced assessment, retaining its position due high scores in satisfaction Conclusions: result underscores importance optimizing solutions, suggesting that tailored, user-focused are pivotal maximizing treatment success adherence.

Язык: Английский

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Innovations in Digital Therapy and Personalized Support

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 153 - 172

Опубликована: Март 6, 2025

Transformative role of machine learning in mental health care, with a focus on digital therapy and personalized support. As challenges increase globally, traditional therapeutic approaches face limitations scalability customization. Machine innovations, such as natural language processing (NLP) predictive analytics, offer new avenues for diagnosis, treatment, ongoing care. AI-powered platforms, including chatbots, provide real-time interventions, while support systems analyze user data to tailor strategies. By identifying patterns behaviors symptoms, enhances the effectiveness treatments, promoting timely individualized However, like privacy, algorithmic bias, potential over-reliance technology must be addressed. these technologies evolve, they significantly improve access quality creating scalable responsive diverse populations.

Язык: Английский

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