Application of AI in Hypertension Health Education: Scoping Review

Assessing the information quality of AI-generated patient educational materials for diabetes: a scoping review

Objective To map the evidence on artificial intelligence (AI)-generated diabetes-related patient education materials and patient-facing health information, with particular attention to AI models, prompting approaches, evaluation methods, and information-quality outcomes. Methods This scoping review was conducted in accordance with the JBI methodology for scoping reviews and reported following the PRISMA-ScR checklist. The review was registered on the Open Science Framework (doi: 10.17605/OSF.IO/U4FAE) PubMed, Web of Science, Embase, Scopus, Cochrane CENTRAL, CNKI, WanFang Data, and SinoMed were searched from inception to May 1, 2026. Chinese- and English-language literature was searched. Two reviewers independently screened studies, charted data, and mapped reported outcomes to Wang and Strong's information quality framework. Outcomes not adequately represented by the framework were retained as additional dimensions. Descriptive statistics and narrative synthesis were used. Results Of 6,049 records identified, 24 studies from 11 countries or regions were included. All studies evaluated ChatGPT or another GPT-family model; 21 used zero-shot or direct prompting, three used role prompting, and two implemented retrieval-augmented generation. Eleven indicators were mapped to the information quality framework, with ease of understanding (n = 14), accuracy (n = 13), and believability (n = 9) assessed most frequently. Six additional outcomes were identified: clinical safety (n = 5), actionability (n = 3), response efficiency (n = 1), personalization (n = 1), transparency (n = 1), and empathy (n = 1). Most studies reported reading demands above those generally recommended for patient education, although findings varied by language, material type, and assessment method. Study-specific instruments were used in 17 studies (70.8%), whereas 10 (41.7%) used structured or established tools. Only six studies reported full source or model blinding, 10 reported quantitative inter-rater agreement, and three involved patients or members of the public. Conclusion Research on AI-generated diabetes education is expanding, but substantial heterogeneity in prompts, evaluators, tools, and outcome definitions limits comparison across studies. Future research should prioritize validated, multilingual, and patient-centered evaluation tools that integrate conventional information-quality attributes with clinically relevant dimensions such as safety, actionability, personalization, transparency, empathy, and response efficiency.

Large Language Models in Medicine: Opportunities, Limitations, and Future Directions – A Scoping Review

A scoping review of 24 PubMed-indexed studies published between 2023 and 2026 was conducted to assess current applications, benefits, limitations, and future directions of LLMs in healthcare.

An Umbrella Review of Artificial Intelligence Applications in Mental Health Care.

BACKGROUND Artificial intelligence (AI) is increasingly used in mental health care to address rising demand, workforce shortages and access barriers; however, evidence remains scattered across multiple systematic reviews, limiting synthesis and practical application. OBJECTIVE To synthesise evidence on AI applications in mental health care, including trends, uses, benefits, challenges and risk-mitigation strategies, guided by an ethics of care framework. METHODS This umbrella review of systematic reviews followed the PRISMA 2020 guidelines. Five databases (Web of Science, Scopus, PubMed, PsycINFO and CINAHL) were searched for peer-reviewed studies published between 2021 and 2025, with additional studies identified through backward and forward citation tracking. Eligible studies were systematic reviews examining artificial intelligence (AI) applications in mental health care involving human participants. Exclusion criteria included non-systematic reviews, non-AI studies, grey literature and non-English publications. Three reviewers independently screened titles, abstracts and full texts according to predefined inclusion and exclusion criteria. Methodological quality was assessed using the AMSTAR-2 tool, and data were extracted on AI type, mental health focus, end-user applications, benefits and challenges. Extracted data were analysed thematically using an iterative coding process to identify recurring patterns on AI type, mental health focus, applications, benefits and challenges. RESULTS Twenty-seven systematic reviews with over 14 million participants were included. Findings converged across four primary themes. First, AI enhances early detection and risk stratification, particularly for depression, anxiety, stress, PTSD and suicidal ideation. Second, AI-enabled digital tools, such as chatbots and mobile platforms, expand access and engagement by supporting symptom monitoring, self-management and continuity of care. Third, structural and ethical fragilities were noted, including data bias, limited external validity, methodological heterogeneity and insufficient transparency. Fourth, AI demonstrated greatest value when embedded within human-centred, hybrid care models that preserve clinical judgement, relational care and accountability. Evidence supporting AI applications was stronger for screening and predictive tasks than for sustained therapeutic interventions. Among the 54 reviews assessed for methodological quality, 27 achieved moderate or high confidence ratings on the AMSTAR-2 tool, with stronger evidence observed in reviews evaluating AI interventions for early detection and risk prediction. CONCLUSION Artificial intelligence (AI) has significant potential to enhance mental health nursing by supporting early identification of symptoms, improving access to care and strengthening clinical decision-making. For nursing practice, responsible implementation requires rigorous validation, equity-focused design, transparency and sustained human oversight. Integrating AI within an ethics-of-care framework ensures that these technologies support person-centred, relational nursing care rather than replacing the critical role of nurses in therapeutic engagement and clinical judgement.

Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices

Abstract Background Sleep disorders represent a significant public health burden associated with cardiovascular and neurocognitive morbidities. While AI technologies offer potential for personalized sleep medicine, clinical integration remains limited. This translational disparity is often attributed to a lack of human-centered design, specifically insufficient stakeholder engagement in the development and implementation of these technologies. Current research frequently prioritizes algorithmic performance over usability and patient trust. Objective This scoping review systematically maps the extent and nature of human-centered AI (HCAI) research within sleep medicine across different AI modalities, evaluating how diverse stakeholders are involved in the design, validation, and implementation of AI tools, including patients, clinicians, and technologists. Methods Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we searched 8 databases (PubMed, Web of Science, Embase, Scopus, IEEE Xplore, ACM Digital Library, APA PsycINFO, and CINAHL) for literature published up to June 18, 2026. We identified primary research describing the design, development, or evaluation of AI technologies for sleep health with explicit human-centered components. Included studies (n=34) were categorized based on AI technology type and the method of stakeholder engagement. Data were extracted and synthesized using a thematic analysis approach. Results Based on the included studies, the analysis reveals an uneven distribution of research focus across technological domains as descriptive patterns rather than definitive trends. Research on generative AI (GenAI) is predominantly restricted to downstream expert auditing of output accuracy (comprising 7/11, 64% of GenAI studies), with a noticeable gap in upstream participatory design involving patients. Conversely, deep learning research primarily focuses on technical explainable AI methods to address algorithmic opacity for clinicians, yet lacks progression to real-world clinical implementation. Mobile health and wearable technologies (17/34, 50%) demonstrate the most balanced HCAI ecosystem, evidencing a complete translational cycle from upstream co-design to downstream clinical implementation. Furthermore, an emerging trend is observed where AI is evolving from an automated diagnostic tool into an interactive therapeutic agent, with recent studies indicating that lay users may perceive responses from large language models as more empathetic than those from physicians. Conclusions Lacking formal quality appraisal, our findings reflect research activity patterns rather than confirmed clinical effectiveness. Nevertheless, this scoping review innovatively applies the HCAI framework to the sleep AI lifecycle. Unlike existing reviews prioritizing algorithmic performance metrics over usability, clinical workflow integration, and patient trust, this study systematically maps these essential sociotechnical factors. It contributes to the field by revealing distinct methodological disparities and the urgent need for upstream participatory design, particularly for GenAI. In the real world, establishing standardized protocols for human-AI interaction, ensuring algorithmic transparency, and addressing demographic biases are essential to foster the clinical trust required for effective AI adoption.

Clinical applications of artificial intelligence in hypertension management: current evidence and future perspectives.

Special Issue on Personalized Management of Obesity (BETTER4U).

BACKGROUND Obesity is a major public health challenge worldwide, driving chronic disease and health care costs. Primary health care (PHC) is a key setting for prevention, early identification, and management of obesity, yet implementation of evidence-based interventions remains limited. Identifying the barriers and enablers that influence adoption of obesity care in PHC is critical to strengthen delivery. OBJECTIVE This scoping review will systematically map and synthesize barriers and enablers to implementing obesity interventions in PHC, considering behavioral, nutritional, pharmacological, digital, and organizational approaches, across both adult and pediatric populations. METHODS The review will follow the Joanna Briggs Institute (JBI) methodology and be reported according to PRISMA-ScR guidelines. Searches will be conducted in PubMed, Cochrane Library, Scopus, Web of Science, and ScienceDirect, complemented by gray literature from OpenGrey, ResearchGate, Google Scholar, and organizational websites. Eligible sources will include peer-reviewed and gray literature published between 2019 and 2025 in English, French, German, Italian, Portuguese, Spanish, or Turkish. Two reviewers will independently screen, extract, and analyze data, with discrepancies resolved by consensus. Data will be synthesized descriptively and thematically, supported by NVivo, and results presented in tables, thematic maps, and conceptual diagrams. Expected Results Anticipated barriers include time constraints, limited provider training, stigma, scarce resources, and inadequate reimbursement models. Enablers are expected to include supportive health policies, training, patient engagement strategies, and team-based care models. CONCLUSIONS This review will synthesize implementation factors affecting obesity care in PHC, informing scalable strategies and policy to improve obesity management worldwide.