Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation

Clinical Infectious Diseases, Год журнала: 2023, Номер 78(4), С. 860 - 866

Опубликована: Ноя. 16, 2023

Abstract Large language models (LLMs) are artificial intelligence systems trained by deep learning algorithms to process natural and generate text responses user prompts. Some approach physician performance on a range of medical challenges, leading some proponents advocate for their potential use in clinical consultation prompting consternation about the future cognitive specialties. However, LLMs currently have limitations that preclude safe deployment performing specialist consultations, including frequent confabulations, lack contextual awareness crucial nuanced diagnostic treatment plans, inscrutable unexplainable training data methods, propensity recapitulate biases. Nonetheless, considering rapid improvement this technology, growing calls integration, healthcare chronically undervalue specialties, it is critical infectious diseases clinicians engage with enable informed advocacy how they should—and shouldn’t—be used augment care.

Язык: Английский

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Chatbot features for anxiety and depression: A scoping review

Health Informatics Journal, Год журнала: 2023, Номер 29(1)

Опубликована: Янв. 1, 2023

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. be affordable efficient on-demand virtual assistants for mental conditions, including anxiety depression. We review features chatbots available or Six bibliographic databases were searched backward forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset this scoping review. Most from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), book chapters (5%, 2/42). About half reviewed had functionality targeting both depression (60%, 25/42), whereas 38% (16/42) targeted only depression, remaining addressed other issues along with Avatars fictional characters rarely used these 26% (11/42) despite their increasing popularity. Mental could benefit helping healthcare workers, Real-time personal assistance fills gap . Their role care is expected increase.

Язык: Английский

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Chatbots’ effectiveness in service recovery

International Journal of Information Management, Год журнала: 2023, Номер 76, С. 102679 - 102679

Опубликована: Июль 11, 2023

Leveraging the computers are social actors theory, in this study, we explore traits of artificial intelligence-based chatbots that make them perceived as trustworthy, drive consumers to forgive firm for service failure, and reduce their propensity spread negative word-of-mouth against firm. Across two scenario-based studies with UK consumers: one a utilitarian product category (n = 586) another hedonic 508), qualitative our findings suggest safety enhances consumers' ability empathy, anthropomorphism benevolence integrity chatbots, i.e., three affect components trustworthiness differently. Further, these have positive influence on customer forgiveness word-of-mouth.

Язык: Английский

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Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e56930 - e56930

Опубликована: Апрель 12, 2024

Background Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various care needs. However, no comprehensive synthesis of chatbots’ roles, users, benefits, limitations is available inform future research application the field. Objective This review aims describe characteristics, focusing on their diverse roles pathway, user groups, limitations. Methods A rapid published literature from 2017 2023 was performed with a search strategy developed collaboration sciences librarian implemented MEDLINE Embase databases. Primary studies reporting chatbot benefits were included. Two reviewers dual-screened results. Extracted data subjected content analysis. Results The categorized into 2 themes: delivery remote services, including patient support, management, education, skills building, behavior promotion, provision administrative assistance providers. User groups spanned across patients chronic conditions well cancer; individuals focused lifestyle improvements; demographic such women, families, older adults. Professionals students also alongside seeking mental behavioral change, educational enhancement. chatbots classified improvement quality efficiency cost-effectiveness delivery. identified encompassed ethical challenges, medicolegal safety concerns, technical difficulties, experience issues, societal economic impacts. Conclusions Health offer wide spectrum applications, potentially impacting aspects care. While they promising for improving quality, integration system must be approached consideration ensure optimal, safe, equitable use.

Язык: Английский

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Mental Health Impacts of Self‐Help Interventions for the Treatment and Prevention of Eating Disorders. A Meta‐Analysis

International Journal of Eating Disorders, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

ABSTRACT Objective Self‐help programs are recommended as a first step in the management of eating disorders. Yet, whether self‐help interventions have broader mental health benefits beyond symptom and risk reduction remains unclear. As randomized controlled trials (RCTs) also assess general secondary to disorder symptoms, we conducted meta‐analysis investigate what extent pure for disorders produce improvements these outcomes. Method Twenty‐seven RCTs prevention or treatment were included. Mean age ranged from 16 46 years. Most based on cognitive‐behavioral therapy. delivered via digital means (Internet, apps, etc.). Random effects meta‐analyses six outcomes: depression, anxiety, distress, quality life, self‐esteem, psychosocial impairment. Analyses stratified pre‐selected (at risk/symptomatic) clinical samples. Results For samples ( k = 18), significant pooled favoring over controls observed depression g 0.24), anxiety 0.23), distress 0.23) self‐esteem 0.18). Effects remained robust when adjusting bias. Non‐significant life Crucially, > 80% waitlist control. 9), found 0.39), impairment 0.29), although results should be interpreted with caution number studies was low. Conclusion small those symptoms that typically comorbid

Язык: Английский

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Co-design of a single session intervention chatbot for people on waitlists for eating disorder treatment: a qualitative interview and workshop study

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

Опубликована: Март 11, 2025

Abstract Background Early treatment is critical to improve eating disorder prognosis. Single session interventions have been proposed as a strategy provide short term support people on waitlists for treatment, however, it not always possible access this early intervention. Conversational artificial intelligence agents or “chatbots” reflect unique opportunity attempt fill gap in service provision. The aim of research was co-design novel chatbot capable delivering single intervention adults the waitlist across diagnostic spectrum and ascertain its preliminary acceptability feasibility. Methods A Double Diamond approach employed which included four phases: discover, define, develop, deliver. There were 17 participants total Australia; ten with lived experience an seven registered psychologists working field disorders, who participated online interviews workshops. Thematic content analyses undertaken interview/workshop transcriptions findings from previous phase informing ideas development next phase. final prototype presented deliver Results identified main themes that present phases interviews/workshops: conversational tone, safety risk management, user journey structure, content. Conclusions Overall, feedback positive throughout process both psychologists. Incorporating allowed refinement chatbot. Further required evaluate chatbot’s efficacy settings.

Язык: Английский

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Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions

Journal of Eating Disorders, Год журнала: 2022, Номер 10(1)

Опубликована: Май 8, 2022

Abstract Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using the field of eating disorders is just beginning to emerge. This paper provides a narrative review existing explores benefits, limitations, ethical considerations aid detection, prevention, treatment disorders. Current primarily uses predict disorder status from females’ responses validated surveys, social media posts, or neuroimaging often with relatively high levels accuracy. early work evidence improve current screening methods. ability these algorithms generalise other samples be used on mass scale only explored. One key benefit over traditional statistical methods simultaneously examine large numbers (100s 1000s) multimodal predictors their complex non-linear interactions, but few studies have explored this Machine also being develop chatbots psychoeducation coping skills training around body image disorders, implications intervention. The use personalise options, ecological momentary interventions, clinicians discussed. accurate, rapid, cost-effective More needed diverse participants ensure that models are unbiased, generalisable all people There important limitations utilising practice. Thus, rather than magical solution, should seen as an tool researchers, eventually clinicians, identification,

Язык: Английский

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Development and usability testing of a chatbot to promote mental health services use among individuals with eating disorders following screening

International Journal of Eating Disorders, Год журнала: 2022, Номер 55(9), С. 1229 - 1244

Опубликована: Авг. 18, 2022

Abstract Objective A significant gap exists between those who need and receive care for eating disorders (EDs). Novel solutions are needed to encourage service use address treatment barriers. This study developed evaluated the usability of a chatbot designed pairing with online ED screening. The tool aimed promote mental health utilization by improving motivation self‐efficacy among individuals EDs. Methods prototype, Alex, was using decision trees theoretically‐informed components: psychoeducation, motivational interviewing, personalized recommendations, repeated administration. Usability testing conducted over four iterative cycles, user feedback informing refinements next iteration. Post‐testing, participants (N= 21) completed System Scale (SUS), Usefulness, Satisfaction, Ease Use Questionnaire (USE), semi‐structured interview. Results Interview detailed aspects enjoyed necessitating improvement. Feedback converged on themes: experience, qualities, content, ease use. Following refinements, users described Alex as humanlike, supportive, encouraging. Content perceived novel personally relevant. USE scores across domains were generally above average (~5 out 7), SUS indicated “good” “excellent” final iteration receiving highest score. Discussion Overall, reflected positively interactions including initial version. Refinements cycles further improved experiences. provides preliminary evidence feasibility acceptance services Public Significance Low rates have been observed following disorder Tools needed, scalable, digital options, that can be easily paired screening, improve addressing utilization.

Язык: Английский

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Project Body Neutrality: Piloting a digital single‐session intervention for adolescent body image and depression

International Journal of Eating Disorders, Год журнала: 2023, Номер 56(8), С. 1554 - 1569

Опубликована: Май 2, 2023

Eating disorders and depression impact youth at alarming rates, yet most adolescents do not access support. Single-session interventions (SSIs) can reach in need. This pilot examines the acceptability utility of a SSI designed to help improve functionality appreciation (a component body neutrality) by focusing on valuing one's based functions it performs, regardless appearance satisfaction.

Язык: Английский

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AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications

Applied Sciences, Год журнала: 2024, Номер 14(13), С. 5889 - 5889

Опубликована: Июль 5, 2024

Mental health disorders are a leading cause of disability worldwide, and there is global shortage mental professionals. AI chatbots have emerged as potential solution, offering accessible scalable interventions. This study aimed to conduct scoping review evaluate the effectiveness feasibility in treating conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, PsycNet, well using AI-powered tools like Microsoft Copilot Consensus. Relevant studies on chatbot interventions for were selected based predefined inclusion exclusion criteria. Data extraction quality assessment performed independently by reviewers. The yielded 15 eligible covering various application areas, such support during COVID-19, specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, promotion, usability assessments. demonstrated benefits improving emotional well-being, addressing conditions, facilitating behavior change. However, challenges related usability, engagement, integration with existing healthcare systems identified. hold promise interventions, but widespread adoption hinges systems. Enhancing personalization context-specific adaptation key. Future research should focus large-scale trials, optimal human–AI integration, ethical social implications.

Язык: Английский

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