Lecture notes on data engineering and communications technologies, Journal Year: 2024, Volume and Issue: unknown, P. 414 - 424
Published: Dec. 31, 2024
Language: Английский
Nature Medicine, Journal Year: 2024, Volume and Issue: 30(2), P. 595 - 602
Published: Feb. 1, 2024
Language: Английский
Citations
26
BMJ Innovations, Journal Year: 2024, Volume and Issue: 10(1-2), P. 4 - 12
Published: Jan. 1, 2024
Mental health services across the globe are overburdened due to increased patient need for psychological therapies and a shortage of qualified mental practitioners. This is unlikely change in short-to-medium term. Digital support urgently needed facilitate access healthcare while creating efficiencies service delivery. In this paper, we evaluate use conversational artificial intelligence (AI) solution ( Limbic Access ) assist both patients practitioners with referral, triage, clinical assessment mild-to-moderate adult illness. Assessing context England’s National Health Service (NHS) Talking Therapies services, demonstrate cohort study design that deploying such an AI associated improved recovery rates. We find those NHS introduced their rates, comparable country reported deteriorating rates during same time period. Further, provide economic analysis indicating usage can be highly cost-effective relative other methods improving Together, these results highlight potential solutions delivery quality care worsening workforce supply system overburdening. For transparency, authors paper declare our conflict interest as employees shareholders Access, referred paper.
Language: Английский
Citations
9
AI, Journal Year: 2025, Volume and Issue: 6(1), P. 14 - 14
Published: Jan. 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.
Language: Английский
Citations
0
Current Psychology, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 16, 2025
Language: Английский
Citations
0
International Journal of Mental Health Nursing, Journal Year: 2025, Volume and Issue: 34(1)
Published: Jan. 23, 2025
ABSTRACT Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of health nurses changed and challenged by AI‐powered cutting‐edge technologies emerging clinical practice. The aim integrative review is to identify synthesise evidence AI‐based applications with relevance for, potential enhance, nursing Five electronic databases (CINAHL, PubMed, PsycINFO, Web Science Scopus) were systematically searched. Seventy‐eight studies identified, critically appraised synthesised following a comprehensive approach. We found that AI use vary widely from machine learning algorithms natural language processing, digital phenotyping, computer vision conversational agents for assessing, diagnosing treating challenges. overarching themes identified: assessment, identification, prediction, optimisation perception reflecting multiple levels embedding AI‐driven practice, how patients staff perceive settings. concluded hold great enhancing However, humanistic approaches may pose some challenges effectively incorporating into nursing. Meaningful conversations between nurses, service users developers should take place shaping co‐creation enhance care way promotes person‐centredness, empowerment active participation.
Language: Английский
Citations
0
Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e60435 - e60435
Published: March 10, 2025
Background Cognitive behavioral therapy (CBT) is a highly effective treatment for depression and anxiety disorders. Nonetheless, substantial proportion of patients do not respond to treatment. The lack engagement with therapeutic materials exercises between sessions, necessary component CBT, key determinant unsuccessful Objective objective this study was test whether the deployment generative artificial intelligence (AI)–enabled support tool, which helps engage in leads improved success patient adherence compared standard delivery CBT through static workbooks. Methods We conducted real-world observational 244 receiving group-based 5 United Kingdom’s National Health Service Talking Therapies services, comparing 150 (61.5%) who used AI-enabled tool 94 (38.5%) exercises. groups were equivalent respect content human-led sessions; however, intervention group received from conducting Results Patients using exhibited greater attendance at sessions fewer dropouts Furthermore, these demonstrated higher reliable improvement, recovery, recovery rates when control group, related degree use tool. Moreover, we found that AI-supported interventions, relative psychoeducational materials, predicted better success, highlighting role personalization intervention’s effectiveness. To investigate mechanisms effects further, separate qualitative experiment nonclinical sample users (n=113). indicated perceived as most useful discussing their problems gain awareness clarity situation well learning how apply coping skills techniques daily lives. Conclusions Our results show an AI-enabled, personalized combination promising avenue improve efficacy mental health care.
Language: Английский
Citations
0
Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2265 - 2265
Published: March 26, 2025
Background/Objectives: This systematic review explores the integration of digital and AI-enhanced cognitive behavioral therapy (CBT) for insomnia, focusing on underlying neurocognitive mechanisms associated clinical outcomes. Insomnia significantly impairs functioning, overall health, quality life. Although traditional CBT has demonstrated efficacy, its scalability ability to deliver individualized care remain limited. Emerging AI-driven interventions-including chatbots, mobile applications, web-based platforms-present innovative avenues delivering more accessible personalized insomnia treatments. Methods: Following PRISMA guidelines, this synthesized findings from 78 studies published between 2004 2024. A search was conducted across PubMed, Scopus, Web Science, PsycINFO. Studies were included based predefined criteria prioritizing randomized controlled trials (RCTs) high-quality empirical research that evaluated AI-augmented interventions targeting sleep disorders, particularly insomnia. Results: The suggest improves parameters, patient adherence, satisfaction, personalization in alignment with individual profiles. Moreover, these technologies address critical limitations conventional CBT, notably those related access scalability. AI-based tools appear especially promising optimizing treatment delivery adapting cognitive-behavioral patterns. Conclusions: While demonstrates strong potential advancing through broader accessibility, several challenges persist. These include uncertainties surrounding long-term practical implementation barriers, ethical considerations. Future large-scale longitudinal is necessary confirm sustained benefits AI-powered
Language: Английский
Citations
0
Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 30, 2025
Language: Английский
Citations
0
International Journal of Systemic Therapy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23
Published: April 24, 2025
Language: Английский
Citations
0
Published: April 10, 2024
Cognitive behavioural therapy (CBT) is a highly effective treatment for depression and anxiety disorders. Nonetheless, substantial proportion of patients do not respond to treatment. The lack engagement with therapeutic materials exercises between sessions - necessary component CBT key determinant unsuccessful Here we tested whether the deployment generative artificial intelligence (AI) powered personalised support tool supporting in leads improved success patient We conducted real-world observational study 137 receiving five UK’s National Health Service (NHS) Talking Therapies services. Ninety-three (68%) used AI-enabled whilst forty-four were provided standard worksheets. Patients using exhibited greater attendance at fewer dropouts from Furthermore, these demonstrated higher reliable improvement, recovery rates when compared control group. Within intervention group, degree usage was directly related number attended reduced dropouts. Our results thus show that an AI-enabled, promising avenue improve outcomes adherence.
Language: Английский
Citations
3