The Influence of AI-Generated Health Information on Medical Consultation Decision-Making Among Adults: A Systematic 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.

Journal of Health Economics and Outcomes Research

A Scoping Review of Shared Decision-Making and Patient Decision Aids for Patients with Peripheral Arterial Disease

Application of AI in Hypertension Health Education: Scoping Review

This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence.

A A Scoping Review of Shared Decision-Making and Patient Decision Aids for Patients with Peripheral Arterial Disease

Objective: To review the current evidence on shared decision-making (SDM) and patient decision aids (PDAs) for patients with peripheral arterial disease (PAD), focusing on application characteristics, intervention effects, and implementation factors to inform clinical decision support models. Methods: A scoping review was conducted following the Joanna Briggs Institute (JBI) framework and PRISMA-ScR guidelines. A systematic search was performed in eight Chinese and English databases, including PubMed, Embase, the Cochrane Library, CINAHL, CNKI, Wanfang, VIP, and the China Biomedical Literature Database, up to 2025. Eligible studies included randomized controlled trials, cross-sectional studies, qualitative studies, and mixed-methods studies. Data were extracted and descriptively synthesized. Results: Fifteen studies published between 2011 and 2025 were included, mainly from Europe, North America, and Asia. Studies explored SDM preferences, the development and application of PDAs, decision support tools, SDM training, multimedia decision-making interventions, and patients’ experiences. SDM and PDAs improved patient knowledge, participation, decision quality, and satisfaction, while reducing decisional conflict and negative emotions. Implementation was affected by time constraints, information complexity, provider–patient communication, and healthcare system factors. Conclusion: SDM and PDA research in PAD is increasing; however, tool standardization and clinical integration remain limited. Future research should develop patient-centered decision-support tools and promote nurse-led SDM models to improve long-term PAD management and patient decision-making experiences.

Enhancing Clinical Decision-Making Using Generative AI-Powered Knowledge Retrieval Systems: A Review of Emerging Approaches and Challenges

The study concludes that evidence-based, auditable, locally adaptable, locally adaptable, and supervised by licensed clinician retrieval systems with generative AI can support safer, faster, and more relevant decision-making processes in clinical settings.