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This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all.

This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL.

Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices.

To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research.

This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

This study aims to address the growing importance of online health information (OHI) and the associated uncertainty. Although previous research has explored factors influencing the credibility of OHI, results have been inconsistent. Therefore, this study aims to identify the essential factors that influence the perceived credibility of OHI by conducting a meta-analysis of articles published from 2010 to 2022. The study also aims to examine the moderating effects of demographic characteristics, study design and the platforms where health information is located.

Based on the Prominence-Interpretation Theory (PIT), a meta-analysis of 25 empirical studies was conducted to explore 12 factors related to information content and source, social interaction, individual and media affordance. Moderators such as age, education level, gender of participants, sample size, platforms and research design were also examined.

Results suggest that all factors, except social support, have significant effects on the credibility of OHI. Among them, argument quality had the strongest correlation with credibility and individual factors were also found to be relevant. Moderating effects indicate that social support was significantly moderated by age and education level. Different sample sizes may lead to variations in the role of social endorsement, while personal involvement was moderated by sample size, platform and study design.

This study enriches the application of PIT in the health domain and provides guidance for scholars to expand the scope of research on factors influencing OHI credibility.

The primary objective was to develop a user-centered mobile health application (app) tailored to the specific health information needs of among immigrant women from diverse backgrounds in Korea.

In-depth interviews were conducted with 24 immigrant women to gain insights into their health information-seeking behavior. Based on the findings, a mobile app was designed and developed. A beta version of the app underwent validation by an MD and seven expert reviewers who assessed the app for content accuracy and conformance to mobile heuristics. Last, immigrant women (n = 12) evaluated the usability of the app.

The study revealed that the interviewed immigrant women had strong health information needs related to pregnancy and parenting. Most of them used multiple sources to find and verify health information. Language barriers were identified as a major obstacle to accessing and evaluating health information. The results of the user test indicated that the app effectively facilitated study participants' search for reliable health information, meeting their specific needs.

This research extended the literature by addressing the limited availability of mobile apps tailored to the health information needs of immigrant women in Korea.

By incorporating multilingual support and focusing on pregnancy and parenting information, the health app serves as a valuable tool to bridge the gap in health information access and to facilitate the well-being of immigrant women in the country.

The internet has evolved into an indispensable platform for seeking health information, particularly among transgender individuals. With an abundance of online resources available, extensive research into the credibility and reliability of this information is essential, as concerns about the quality of online resources persist. Transgender individuals are drawn to online health information due to the anonymity it offers, providing them with a sense of freedom from social isolation and the discomfort of experimenting with their transgender identity. However, it is crucial to assess the accuracy and reliability of the transgender health information available on the internet. This article aims to evaluate the quality of online transgender health resources by utilizing ten credibility indicators, along with six indicators to assess the veracity of the content.

A total of 179 online resources were meticulously reviewed after excluding any unnecessary and irrelevant ones, to ensure a comprehensive assessment.

The findings suggest that among the chosen resources, none of them meet all the criteria for maintaining high standards of accuracy and reliability in health information. In other words, none of these sources completely adhere to the established measures for ensuring that the information they provide is trustworthy and of high quality in the context of health.

The study provides valuable insights into the online realm of transgender health information, revealing both the strengths and weaknesses of the existing resources. By pinpointing areas that need enhancement and showcasing commendable practices, this research strives to promote a more knowledgeable and supportive online environment for individuals in search of transgender health information.

The higher-level aim of this study is to investigate the impact of health information needs satisfaction on the fear of COVID-19 for the general population. The investigation is theoretically grounded on Wilsons’ model of information seeking in the context of inquesting the reasons for seeking health information as well as the information sources the general population deploy during the COVID-19 pandemic.

This cross-sectional survey examines the correlations between health information seeking behavior and the COVID-19 generated fear in the general population through the application of a specially designed structured questionnaire which was distributed online. The questionnaire comprised four main distinct research dimensions (i.e. information needs, information sources, obstacles when seeking information and COVID-19 generated fear) that present significant validity levels.

Individuals were motivated to seek COVID-related health information to cope with the pandemic generated uncertainty. Information needs satisfaction as well as digital health literacy levels is associated with the COVID-19 generated fear in the general population. Finally, a conceptual framework based on Wilsons’ macro-model for information seeking behavior was developed to illustrate information needs satisfaction during the pandemic period. These results indicate the need for incentives to enhance health information needs satisfaction appropriately.

The COVID-19 generated fear in the general population is studied through the information seeking behavior lenses. A well-studied theoretical model for information seeking behavior is adopted for health-related information seeking during pandemic. Finally, digital health information literacy levels are also associated with the fear of COVID-19 reported in the authors’ survey.

Patient-centric exchanges, a major type of Health Information Exchange (HIE), empower patients to aggregate and manage their health information. This exchange model helps patients access, modify and share their medical information with multiple healthcare organizations. Although existing studies examine patient engagement, more research is required to investigate patients' attitudes and willingness to play an active role in patient-centered information exchange. The study's main objective is to develop a model based on the belief-attitude-intention paradigm to empirically examine the effects of patients' attitudes toward engagement in care on their willingness to participate in patient-centric HIE.

The authors conducted an online survey study to identify the antecedents and consequences of patients' attitudes toward engagement in care. To empirically test the research model, the authors collected data from a national sample (n = 357) of individuals in the United States. The data were analyzed using structural equation modeling (SEM).

The proposed model categorizes the antecedents to patients' attitudes toward engagement in patient-related and healthcare system factors. The results show that patient-related factors (perceived health literacy and perceived coping ability) and health system factors (perceived experience with the healthcare organization and perceived patient-provider interaction) significantly shape patient attitude toward care management engagement. The results indicate that patients' attitudes toward engaging in their healthcare significantly contribute to their willingness to participate in medical information sharing through patient-centric HIE initiatives. Moreover, the authors’ findings also demonstrate that the link between patient engagement and willingness to participate in HIE is stronger for individuals who perceive lower levels of privacy and security concerns.

The authors validate the proposed model explaining patients' perceptions about their characteristics and the healthcare system significantly influence their attitude toward engaging in their care. This study also suggests that patients' favorable attitude toward engagement can bring patient-centric HIE efforts onto a path to success. The authors’ research attempts to shed light on the importance of patients' roles in adopting patient-centric HIE initiatives. Theoretical and practical contributions of this study are noticeable since they could result in a deeper understanding of the concept of patient engagement and how it may affect healthcare services in an evolving digital world. The authors’ findings can help healthcare organizations provide public citizen-centric services by introducing user-oriented approaches in healthcare delivery systems.

Health misinformation on social media threatens public health. A critical question that sheds light on the propagation of health misinformation across social media platforms revolves around identifying the specific types of social media users susceptible to this issue. This study provides an initial insight into this matter by examining the underlying psychological mechanism that renders users susceptible to health misinformation.

In this study, we developed an integrated model of susceptibility to health misinformation, drawing on the motivation-opportunity-ability theory and the elaboration likelihood model. We collected the data from a sample of 342 social media users in China. Furthermore, the fuzzy-set qualitative comparative analysis was adopted to examine the proposed model and uncover the causal recipes associated with susceptibility to health misinformation.

The results indicated that there are three configural types of users that are susceptible to health misinformation: the health-consciousness core-driven type, the popularity-driven core type and the dual-driven type characterized by both high health consciousness and information popularity. Among these, high health-consciousness and the reliance on information popularity-based pathways emerge as pivotal factors influencing susceptibility to health misinformation.

This study contributes to the social media literature by identifying various psychological traits that lead to social media users’ susceptibility to health misinformation. Additionally, the study provides comprehensive guidance on how to mitigate the spread of health misinformation.

The environment in which users acquire medical and health information has changed dramatically, with online health communities (OHCs) emerging as an essential means for accessing health information. It is imperative to comprehend the factors that shape the users' compliance willingness (UCW) to health information in OHCs.

This study adopted the information adoption model (IAM) and theory of planned behavior (TPB) to investigate the influence of argument quality (AQ), source credibility (SC) and subjective norms (SN) on UCW while considering the two types of online health information – mature and emerging treatments. The authors conducted an explanatory-predictive study based on a 2 (treatment types: mature vs. emerging) * 2 (AQ: high vs. low) * 2 (SC: high vs. low) scenario-based experiment, using the partial least squares structural equation modeling (PLS-SEM).

SC positively influences AQ. AQ, SC and SN contribute to information usefulness (IU). These factors positively affect UCW through the mediation of IU. SN were found to improve UCW directly. Moreover, the moderating effect of SC on AQ and IU was more substantial for emerging treatments.

The research model integrates IAM and TPB, considering information types as an additional variable. The approach and findings provide a valuable explanation for UCW to health information in OHCs.

This study was carried out to examine the volume and annual growth pattern of research on e-health literacy research, investigate the open-access types of e-health literacy research and perform document production by country and by sources. The study also mapped the keywords used by authors to represent e-health literacy research and performed an analysis of the clusters of the keywords to reveal the thematic focus of research in the area.

The research was guided by a bibliometric approach involving visualization using VosViewer. Data were sourced from Scopus database using a syntax that was tested and verified to be capable of yielding reliable data on the subject matter. The analysis in this study was based on bibliographic data and keywords.

A total number of 1,176 documents were produced during 2006 and 2022. The majority of the documents (18.90%) were published based on hybrid open-access processes, and the USA has the highest contributions. The Journal of Medical Internet Research is the venue for most of the documents on the subject. The 1,176 documents were described by 5,047 keywords, 4.29 keywords per document, and the keywords were classified into five clusters that aptly capture the thematic structure of research in the area.

e-Health literacy has experienced significant growth in research production from 2006 to 2022, with an average of 69 documents per year. Research on e-health literacy initially had low output but began to increase in 2018. The majority of e-health literacy documents are available through open access, with the USA being the leading contributor. The analysis of keywords reveals the multifaceted nature of e-health literacy, including access to information, attitudes, measurement tools, awareness, age factors and communication. Clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers and effective communication in healthcare.

This study has practical implications for health promotion. There is also the element of patient empowerment in which case patients are allowed to take an active role in their healthcare. By understanding their health information and having access to resources that help them manage their conditions, patients can make informed decisions about their healthcare. Finally, there is the issue of improved health outcomes which can be achieved by improving patients' e-health literacy. Visualisation of e-health literacy can help bridge the gap between patients and healthcare providers, promote patient-centered care and improve health outcomes.

Research production on e-Health literacy has experienced significant growth from 2006 to 2022, with an average of 69 documents per year. Many e-health literacy documents are available through open access, and the USA is the leading contributor. The analysis of keywords reveals the nature of e-health literacy, including access to information, attitudes, measurement tools, awareness and communication. The clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers, and effective communication in healthcare.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.