Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions – A Narrative Review for a Comprehensive Insight

Risk Management and Healthcare Policy, Год журнала: 2024, Номер Volume 17, С. 1339 - 1348

Опубликована: Май 1, 2024

Abstract: Mental health is an essential component of the and well-being a person community, it critical for individual, society, socio-economic development any country. healthcare currently in sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping screening, diagnosis, treatment modalities psychiatric illnesses. The present narrative review aimed at discussing current landscape role AI mental healthcare, including treatment. Furthermore, this attempted to highlight key challenges, limitations, prospects providing based on existing works literature. literature search was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web Science, IEEE Xplore, we included only English-language articles published last five years. Keywords used combination Boolean operators ("AND" "OR") were following: "Artificial intelligence", "Machine learning", Deep "Early diagnosis", "Treatment", "interventions", "ethical consideration", "mental Healthcare". Our revealed that, equipped predictive analytics capabilities, can improve planning by predicting individual's response various interventions. Predictive analytics, which uses historical data formulate preventative interventions, aligns move toward individualized preventive healthcare. In screening diagnostic domains, subset AI, machine learning deep learning, has been proven analyze sets predict patterns associated problems. However, limited studies have evaluated collaboration between professionals delivering these sensitive problems require empathy, human connections, holistic, personalized, multidisciplinary approaches. Ethical issues, cybersecurity, lack diversity, cultural sensitivity, language barriers remain concerns implementing futuristic approach Considering approaches, imperative explore aspects. Therefore, future comparative trials larger sample sizes are warranted evaluate different models across regions fill knowledge gaps. Keywords: intelligence, early interventions

Язык: Английский

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Algorithm‐based modular psychotherapy vs. cognitive‐behavioral therapy for patients with depression, psychiatric comorbidities and early trauma: a proof‐of‐concept randomized controlled trial

World Psychiatry, Год журнала: 2024, Номер 23(2), С. 257 - 266

Опубликована: Май 10, 2024

Effect sizes of psychotherapies currently stagnate at a low‐to‐moderate level. Personalizing psychotherapy by algorithm‐based modular procedures promises improved outcomes, greater flexibility, and better fit between research practice. However, evidence for the feasibility efficacy modular‐based psychotherapy, using personalized treatment algorithm, is lacking. This proof‐of‐concept randomized controlled trial was conducted in 70 adult outpatients with primary DSM‐5 diagnosis major depressive disorder, score higher than 18 on 24‐item Hamilton Rating Scale Depression (HRSD‐24), least one comorbid psychiatric according to Structured Clinical Interview (SCID‐5), history “moderate severe” childhood maltreatment domain Childhood Trauma Questionnaire (CTQ), exceeding cut‐off value three measures early trauma‐related transdiagnostic mechanisms: Rejection Sensitivity (RSQ), Interpersonal Reactivity Index (IRI), Difficulties Emotion Regulation Scale‐16 (DERS‐16). Patients were 20 sessions either standard cognitive‐behavioral therapy alone (CBT) or CBT plus modules mechanism‐based algorithm (MoBa), over 16 weeks. We aimed assess MoBa, compare MoBa vs. respect participants’ therapists’ overall satisfaction ratings therapeutic alliance (using Working Alliance Inventory ‐ Short Revised, WAI‐SR), efficacy, impact mechanisms, safety. The outcome HRSD‐24 post‐treatment. Secondary outcomes included, among others, rate response (defined as reduction 50% from baseline <16 post‐treatment), remission ≤8 improvements mechanisms social threat response, hyperarousal, processes/empathy. found no difficulties selection individual patients, applying above‐mentioned cut‐offs, implementation MoBa. Both participants therapists reported had WAI‐SR CBT. approaches led reductions symptoms post‐treatment, non‐significant superiority nearly times likely experience end (29.4% 11.4%; odds ratio, OR = 3.2, 95% CI: 0.9‐11.6). Among patients showed significantly post‐treatment effect processes/empathy (p<0.05) compared who presented an exacerbation this Substantially less adverse events These results suggest acceptability complementing depressed comorbidities trauma. While initial observed, potential clinical advantages interindividual heterogeneity will have be investigated fully powered confirmation trials.

Язык: Английский

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Primary-level and community worker interventions for the prevention of mental disorders and the promotion of well-being in low- and middle-income countries

Cochrane library, Год журнала: 2023, Номер 2023(10)

Опубликована: Окт. 24, 2023

Язык: Английский

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Psychological and social interventions for the promotion of mental health in people living in low- and middle-income countries affected by humanitarian crises

Cochrane library, Год журнала: 2024, Номер 2024(5)

Опубликована: Май 21, 2024

Язык: Английский

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The digital cumulative complexity model: a framework for improving engagement in digital mental health interventions

Frontiers in Psychiatry, Год журнала: 2024, Номер 15

Опубликована: Сен. 3, 2024

Mental health disorders affect a substantial portion of the global population. Despite preferences for psychotherapy, access remains limited due to various barriers. Digital mental interventions (DMHIs) have emerged increase accessibility, yet engagement and treatment completion rates are concerning. Evidence across healthcare where some degree self-management is required show that negatively influenced by contextual complexity. This article examines non-random factors influencing patient in digital face-to-face psychological therapies. It reviews established models introduces an adapted version Cumulative Complexity Model (CuCoM) as framework understanding context health. Theoretical like Fogg Behavior Model, Persuasive System Design, Self-Determination Theory, Supportive Accountability aim explain disengagement. However, none adequately consider these broader their complex interactions with personal characteristics, intervention requirements technology features. We expand on proposing application CuCoM’s contexts (known DiCuCoM), focusing interplay between burden, capacity, demands. Standardized DMHIs often fail individual variations burden leading variation. DiCuCoM highlights need balancing workload capacity improve engagement. Factors such life demands, treatment, examined influence adherence. The proposes person-centered approach informed CuCoM Minimally Disruptive Medicine, emphasizing systems acknowledge address unique burdens capacities individuals. Strategies enhancing include assessing reducing utilizing predict respond New could lead better ultimately outcomes.

Язык: Английский

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The Wither or Thrive Model of Resilience: an Integrative Framework of Dynamic Vulnerability and Resilience in the Face of Repeated Stressors During the COVID-19 Pandemic

Adversity and Resilience Science, Год журнала: 2022, Номер 3(4), С. 261 - 282

Опубликована: Июль 13, 2022

Abstract During the first 2 years of COVID-19 pandemic, empirical efforts in psychological sciences have been unequivocally focused on understanding psychosocial impact resilience and vulnerability. While current work is guided by different existing theoretical models vulnerability, emerging datasets also pointed to a necessity for an update these models. Due unique features developments specific pandemic such as occurrence repeated collective stressors varying durations, position paper, we introduce Wither or Thrive model Resilience (With:Resilience). It integrates key aspects prevailing frameworks within context extends them (1) moving away from single scale approaches towards higher-order latent expression vulnerability incorporating non-clinical mental health markers, (2) proposing trajectories resilience-vulnerability across over long periods time, (3) multiple influencing factors including socio-economic concept social cohesion well separate mediating processing mechanisms. We propose that With:Resilience will enable more nuanced approach appropriate analytical investigation vast incoming data during suggest some concrete methodological approaches. This framework assist development actionable public guidelines society present future contexts aid policy making interventional aimed at protecting most vulnerable amongst us.

Язык: Английский

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Artificial intelligence in precision space health

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 103 - 115

Опубликована: Янв. 1, 2025

Язык: Английский

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Dance and Stress Regulation: A Multidisciplinary Narrative Review

Psychology of sport and exercise, Год журнала: 2025, Номер unknown, С. 102823 - 102823

Опубликована: Фев. 1, 2025

Язык: Английский

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Treating the individual: moving towards personalised eating disorder care

Journal of Eating Disorders, Год журнала: 2025, Номер 13(1)

Опубликована: Апрель 9, 2025

Eating disorders (EDs) are complex and heterogeneous conditions, which often not resolved with conventional, manualised treatments. Arguments for the development of holistic, person-centred treatments accounting individual variability have been mounting amongst researchers, clinicians people lived experience alike. This review explores transformative potential personalised medicine in ED care, emphasising integration precision diagnostics tailored interventions based on genetic, biological, psychological environmental profiles. Building advancements genomics, neurobiology, computational technologies, it advocates a shift from categorical diagnostic frameworks to symptom-based dimensional approaches. The paper summarises emerging evidence supporting psychiatry, including biomarkers, patient-reported outcomes, predictive modelling, staging models, discusses their application research clinical care. It highlights utility machine learning idiographic statistical methods optimising therapeutic outcomes identifies key challenges, such as ethical considerations, scalability implementation.

Язык: Английский

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Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale

Perspectives on Psychological Science, Год журнала: 2023, Номер 19(6), С. 1031 - 1056

Опубликована: Авг. 29, 2023

With the rapidly growing availability of scalable psychological assessments, personality science holds great promise for scientific study and applied use customized behavior-change interventions. To facilitate this development, we propose a classification system that divides targeting into two approaches differ in process by which interventions are designed: audience-to-content matching or content-to-audience matching. This is both integrative generative: It allows us to (a) integrate existing research on personalized from different subdisciplines (e.g., political, educational, organizational, consumer, clinical health psychology) (b) articulate open questions generate promising new avenues future research. Our objective infuse intervention encourage cross-disciplinary collaborations within outside psychology. ensure development personality-customized aligns with broader interests individuals (and society at large), also address important ethical considerations privacy, self-determination, equity) offer concrete guidelines researchers practitioners.

Язык: Английский

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