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Artificial intelligence-based conversational agents (CAs) have shown transformative potential in healthcare, yet their application in cancer health education has remained underexplored, particularly regarding usability and patients' experiences. Existing reviews lack a dedicated focus on user perspectives, limiting insights into how CAs can be optimised for patient needs.
To explore the usability and experience of artificial intelligence-based conversational agents in health education for cancer from the user perspective.
A scoping review was conducted with the Joanna Briggs Institute Scoping Reviews conduct guidance and reported according to the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews checklist.
A search was performed in PubMed, Embase, CINAHL, Web of Science, PsycINFO, IEEE Xplore Digital Library and ACM Digital Library from their inception to March 6, 2024. The references to the articles included were also searched. The Pillar Integration Process was employed to chart data.
A total of 12 studies were included in this scoping review, which revealed that CAs supported diverse educational contexts, including cancer-related knowledge (41.7%), pretest genetics (33.3%), self-management (16.7%) and psychological skills (8.3%). Three studies reported that patients preferred interactions with multiple options or ‘read more’ functions. Patients were generally optimistic about the CAs and reported that CAs provided informational, physical, and psychological support for them. However, limitations such as insufficient customisation, lack of empathy, and defects in understanding free-input questions were noted.
This review demonstrated that CAs are promising complementary tools in cancer education, alleviating healthcare burdens while enhancing patient engagement, which was particularly critical in resource-limited settings. However, clinical implementation requires more rigorous validation of safety protocols and high-quality original studies.
Nurses and policymakers should consider CAs valuable tools to enhance cancer health education, provided that they align with patient needs and institutional safety standards.
To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.
Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.
Systematic review.
We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.
The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high-risk for bias.
According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.
Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high-risk individuals and key factors associated with low medication adherence in chronic diseases.
This systematic review was conducted without patient or public participation.
Oral mucosa is an ideal model for studying scarless wound healing. Researchers have shown that the key factors which promote scarless wound healing already exist in basal state of oral mucosa. Thus, to identify the other potential factors in basal state of oral mucosa will benefit to skin wound healing. In this study, we identified eight gene modules enriched in wound healing stages of human skin and oral mucosa through co-expression analysis, among which the module M8 was only module enriched in basal state of oral mucosa, indicating that the genes in module M8 may have key factors mediating scarless wound healing. Through bioinformatic analysis of genes in module M8, we found IGF2 may be the key factor mediating scarless wound healing of oral mucosa. Then, we purified IGF2 protein by prokaryotic expression, and we found that IGF2 could promote the proliferation and migration of HaCaT cells. Moreover, IGF2 promoted wound re-epithelialization and accelerated wound healing in a full-thickness skin wound model. Our findings identified IGF2 as a factor to promote skin wound healing which provide a potential target for wound healing therapy in clinic.
The aim of this study is to evaluate the relative merits of various heart failure models of care with regard to a variety of outcomes.
Systematic review.
Five databases including PubMed, Web of Science, Medline, Embase and Science Direct were searched from the inception date of databases to August 20, 2022.
This review used the Cochrane Collaboration's ‘Risk of Bias’ tool to assess quality. Only randomised controlled trails were included in this review that assessed all care models in the management of adults with heart failure. A categorical summary of the pattern of the papers was found, followed by extraction of outcome indicators.
Twenty articles (19 studies) were included. Seven examined nurse-led care, two examined multidisciplinary specialist care, nine (10 articles) examined patient self-management, and one examined nurse and physiotherapist co-led care. Regarding outcomes, this review examined how well the four models performed with regard to quality of life, health services use, HF self-care, and anxiety and depression for heart failure patients. The model of patient self-management showed more beneficial results than nurse-led care, multidisciplinary specialist care, and nurse and physiotherapist co-led care in reducing hospital days, improving symptoms, promoting self-care behaviours of HF patients, enhancing the quality of life, and strengthening self-care ability.
This systematic review synthesises the different care models and their relative effectiveness. Four different models of care were summarised. Of these models, the self-management model demonstrated better outcomes.
The self-management model is more effective in increasing self-management behaviours and self-management abilities, lowering the risk of hospitalisation and death, improving quality of life, and relieving anxiety and depression than other models.
There was no funding to remunerate a patient/member of the public for this review.
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