Digital divides in verification: investigating the heterogeneity of verification patterns for AI-generated health information

To describe patterns of 'online' and 'offline' health information seeking in families with children under five years of age and living in five socially, economically and culturally disparate local authority (LA) wards in one inner-city area. Earlier work analysed data from the five LA wards merged as one data set. A 'digital divide' in health information seeking was identified between parents who actively sought information from both internet websites and from 14 other health information sources (online health information seekers), and those who acquired information from a more limited range of sources excluding the internet. Of the two groups, the online health information seekers had higher levels of computer ownership and, therefore, internet access within the home. Re-analysis of data (questionnaires n = 224; five focus groups; two interviews with service providers; two opportunistic conversations with service providers). Additional data were retrieved after the original data analysis and between 2005 and 2007. These data were from service user-led discussions (n = 30) held with parents in child health clinics, informal interviews (n = 11) with health visitors and semi-structured interviews (n = 2) with health visitors. Information was also retrieved from the Office for National Statistics data set. In the re-analysis, data were disaggregated at LA ward level in order to explore local influences on patterns of health information seeking. Multiple layers of influence upon parental health information seeking emerged and revealed a non-digital second divide, which was independent of computer ownership and home internet access. This divide was based on preference for use of certain health information sources, which might be either 'online' or 'offline'. A spatial patterning of both digital and preferential divides was identified with an association between each of these and features of the physical, social, cultural and psychosocial environment, one of which was perceived access to primary health care. Complex patterns of health information seeking relate to each of the 'divides'--digital and preferential. Patterns of health information seeking reflect differing perceptions of information availability and usefulness as experienced by parents within their local physical, social, cultural and psychosocial worlds. Access to primary care services is a key component of this local environment.

The literature has explored age differences in health information seeking during the COVID-19 pandemic. However, there is a noticeable gap in research regarding generational variations in the underlying factors of health information scanning and sharing, as well as generational differences in the interplay of health information seeking, scanning, and sharing. This study examined: (1) differences in risk- and channel-related motivators of online health information seeking, online health information scanning, and COVID-19 information sharing among three generational cohorts: Baby Boomers, Generation X, and Millennials; and (2) generational differences in the relationship between information seeking, scanning, and sharing. The focus on generational differences took into consideration both biological and social differences in age cohorts when comparing their information behaviors. The data came from an online survey of 1,004 Hong Kong residents. Results showed generational similarities in individuals’ more frequent information scanning than seeking and the positive relationship between information seeking and sharing. Generational differences emerged in several aspects, including the frequency of information seeking and scanning; the relationship between health status and information seeking; associations of income, health status, channel characteristics, and channel utility with information scanning; and associations of information seeking and scanning with information sharing. These findings offer insights into how risk- and channel-related factors may differ among generations or transcend generational differences in shaping individuals’ information behaviors in the historical and cultural context of COVID-19 in Hong Kong. Results of our study inform communication strategies for different generational groups in future public health crises.

BackgroundGeographically isolated Hispanic populations, such as those living in Puerto Rico, may face unique barriers to health information access. However, little is known about health information access and health information-seeking behaviors of this population.ObjectiveTo examine differences in health and cancer information seeking among survey respondents who ever used the Internet and those who did not, and to explore sociodemographic and geographic trends.MethodsData for our analyses were from a special implementation of the Health Information National Trends Survey conducted in Puerto Rico in 2009. We collected data through random digit dialing, computer-assisted telephone interviews (N = 639). The sample was drawn from the eight geographic regions of the Puerto Rico Department of Health. To account for complex survey design and perform weighted analyses to obtain population estimates, we analyzed the data using SUDAAN. Frequencies, cross-tabulation with chi-square, and logistic regression analyses were conducted. Geographic information system maps were developed to examine geographic distributions of Internet use and information seeking.ResultsOf 639 participants, 142 (weighted percentage 32.7%) indicated that they had ever gone online to access the Internet or World Wide Web; this proportion was substantially lower than that of US mainland Hispanics who reported using the Internet (49%). While 101 of 142 (weighted percentage 59.6%) respondents who used the Web had ever sought health information, only 118 of 497 (weighted percentage 20.0%) of those who did not use the Web had sought health information. The pattern was similar for cancer information: 76 of 142 respondents (weighted percentage 47.2%) who used the Web had ever sought cancer information compared with 105 of 497 (weighted percentage 18.8%) of those who had not used the Web. These results were slightly lower but generally consistent with US mainland Hispanics’ health (50.9%) and cancer (26.4%) information seeking. Results of separate logistic regression models controlling for sociodemographic characteristics demonstrated that, compared with individuals who did not seek health or cancer information, those who did were over 5 times as likely to have used the Internet (odds ratio 5.11, P < .001). Those who sought cancer information were over twice as likely to have used the Internet (odds ratio 2.5, P < .05). The frequency of Internet use and health and cancer information seeking was higher in the San Juan metro region than in more rural areas.ConclusionsOur results contribute to the evidence base for health and cancer communication planning for Puerto Rico, and suggest that health education and outreach efforts should explore the use of available and trusted methods of dissemination such as radio and television, as well as community-based health care providers and organizations, to supplement and encourage use of the Internet as a source of health information.

The Guam population offers a unique glimpse into Americans of Pacific Island ancestry and their communication and information-seeking behaviors, experiences, and needs relevant to cancer. National surveys do not typically include the U.S. territories, so there are limited data on the health and cancer information–seeking behaviors of these populations, in which health disparities persist. To fill this information gap, we conducted a survey on health communication in Guam using a modified version of the Health Information National Trends Survey instrument supplemented with items measuring specific cultural factors and communication practices. The results of the survey (N = 511) revealed some differences in health and cancer information–seeking patterns in Guam and the mainland United States. Sociodemographic variables, including sex, age, education, income, and employment, were significantly associated with health and cancer information seeking and Internet use. Levels of trust in various information sources were differentiated in the Guam and mainland U.S. samples. Logistic regression models revealed differences in factors predicting health and cancer information seeking and Internet use. The results suggest that these health information–seeking patterns and factors should be taken into account when developing communication strategies for more effective prevention and control programs.

Effective screening tools are available for many of the top cancer killers in the USA. Searching for health information has previously been found to be associated with adhering to cancer screening guidelines, but Internet information seeking has not been examined separately. The current study examines the relationship between health and cancer Internet information seeking and adherence to cancer screening guidelines for breast, cervical, and colorectal cancer in a large nationally representative dataset. The current study was conducted using data from the Health Information National Trends Survey from 2003 and 2007. The study examined age-stratified models which correlated health and cancer information seeking with getting breast, cervical, and colorectal cancer screening on schedule, while controlling for several key variables. Internet health and cancer information seeking was positively associated with getting Pap screening on schedule, while information seeking from any sources was positively associated with getting colorectal screening on schedule. People who look for health or cancer information are more likely to get screened on schedule. Some groups of people, however, do not exhibit this relationship and, thus, may be more vulnerable to under-screening. These groups may benefit more from targeted interventions that attempt to engage people in their health care more actively.

The incorporation of Generative Artificial Intelligence (GenAI) in education offers new opportunities to enhance students’ learning experiences. Using a Chi-square Automatic Interaction Detection (CHAID) analysis, this study examined how the frequency of GenAI use for higher-order learning tasks and for supporting learning, as well as various demographic factors, influence students’ attitudes towards GenAI. The first decision tree analysis revealed that the respondents’ GenAI usage frequency for higher-order learning was the most important factor determining their desire to see GenAI incorporated into the university’s curriculum and assessment. In addition, for some learners, the study found that age was a significant factor, with the younger learners having a more positive attitude towards this technology than those who were older. An analysis of the second decision tree found that the frequency of GenAI use for learning support was the most important determinant of the students’ willingness to have GenAI mark their assignments. An understanding of how demographic and contextual factors influence the students’ attitudes towards the role of GenAI in education can guide academic institutions and educators in the development of effective educational strategies and policies that facilitate its acceptance by a diverse student population.

Although cancer population is growing, very few research efforts have been devoted to understanding the impacts of cancer cognitions and emotion on both cancer information and general health information seeking behaviors among people without cancer diagnosis using a nationally representative survey. The present study drew on data from 2007 Health Information National Trends Survey and built two hierarchical logistic regression models to elucidate the differences between information seekers and non-seekers in terms of sociodemographic, cancer cognitions (controllability and locus of causation) and worry. Controllability, a motivational construct for predicting adaptive behaviors, was statistically significantly associated with cancer information seeking (OR=1.38) and general health information seeking (OR=1.67); i.e., the more the respondents thought they can do to prevent cancer, the more likely they sought information. Cancer worry, a general and normative worry about developing cancer, is statistically significantly associated with cancer information seeking (OR=0.68); i.e., the more frequent the respondents worry about getting cancer, the more likely they sought cancer information. Locus of causation, measuring whether people make internal or external attribution, was not statistically significantly associated with cancer information seeking; but people who attribute causes of cancer to their own behavior or lifestyle are more likely to seek information. Cancer information seekers are more likely to be older (OR=0.99), female (OR=0.63), Non-Hispanic White (OR=1.48), married (OR=1.06), more highly educated (OR=0.76), and with family cancer history (OR=2.16). Results for general health information seeking were presented in the following table. Results of this study are generalizable for considering how to raise public awareness of cancer prevention and allocate health educational resources. Table 1. Hierarchal Logistic Regression Models of Health Information Seeking Variable B OR 95% CI Age*** 0.013 1.013 (1.009, 1.017) Gender*** −0.546 0.579 (0.506, 0.663) Race*** Hispanic*** 1.132 3.101 (1.700, 5.657) Non-Hispanic White*** 0.743 2.101 (1.185, 3.727) Black or African American** 0.938 2.555 (1.404, 4.651) American Indian/Alaska Native*** 1.419 4.132 (1.749, 9.764) Asian*** 1.569 4.802 (2.472, 9.328) Native Hawaiian/Pacific Islander −0.356 0.701 (0.123, 3.980) Marital status*** 0.126 1.134 (1.095, 1.174) Education*** −0.462 0.630 (0.603, 0.658) Family history of Cancer*** 0.324 1.382 (1.199, 1.594) Controllability*** 0.510 1.665 (1.296, 2.139) Cancer worry −0.099 0.906 (0.648, 1.266) Locus of causation 0.138 1.148 (0.927, 1.422) Controllability.Cancer worry −0.036 0.964 (0.878, 1.059) Controllability.Locus of causation −0.050 0.951 (0.887, 1.019) Cancer worry.Locus of causation −0.042 0.959 (0.871, 1.055) Note a. Reported values are coefficients in the third block of the regression analysis Note b. * p&amp;lt;.05; ** p&amp;lt;.01; *** p&amp;lt;.001 Note c. This table contains results from the second hierarchical logistic regression model of GENERAL HEALTH information seeking with sociodemographic, cancer cognition, and emotion variables. Given the space limitation, I reported results from the first hierarchical logistic regression model (CANCER INFORMATION seeking) in the above abstract. Citation Format: Xiaofei He. The roles of cancer worry and attribution in health and cancer information seeking: An analysis of 2007 Health Information National Trends Survey (HINTS). [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1371. doi:10.1158/1538-7445.AM2013-1371

Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH's health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH's self-reports of internet use. The YARH were aged 14-24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH's lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥ .75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms.

Purpose This study examines the integration of generative and predictive artificial intelligence (AI) models within smart cities, focusing on how user readiness and technology adoption influence their contribution to sustainable urban development and governance. Design/methodology/approach The study applies a systematic literature review following PRISMA guidelines and synthesizes evidence from 50 peer-reviewed studies (2018–2025) indexed in Scopus and Web of Science. It combines bibliometric mapping using VOSviewer with thematic analysis to examine the drivers, barriers and governance mechanisms shaping the adoption of generative, predictive and hybrid applications in urban contexts. Findings Generative AI fosters participatory engagement, citizen co-design and interactive simulations, advancing SDG 11 (Sustainable Cities and Communities) and SDG 4 (Quality Education) through enhanced digital literacy and inclusive planning. Predictive AI improves operational efficiency, forecasting accuracy and data-driven policymaking, supporting SDG 9 (Industry, Innovation and Infrastructure) and SDG 13 (Climate Action) by promoting sustainable resource use and climate-resilient management. Hybrid AI integrates these strengths, addressing both social and operational aspects of smart city development and aligning with SDG 17 (Partnerships for the Goals) through cross-sector collaboration and shared governance. Collectively, these models contribute to broader sustainability goals, including SDGs 3, 7 and 12. Research limitations/implications This review acknowledges several key limitations. Reliance on Scopus and Web of Science may exclude regionally significant or domain-specific studies not indexed in these databases. The focus on English-language publications introduces potential language bias, possibly overlooking relevant research from non-English-speaking regions. Restricting the timeframe to 2018–2025 captures recent developments but may omit earlier foundational work or the most recent studies not yet indexed. Differences in research design, policy contexts and sample characteristics also affect comparability and limit generalizability. Future research should broaden data sources, include multilingual literature and adopt mixed-methods and longitudinal approaches to enhance contextual diversity and empirical robustness. Practical implications The findings provide practical guidance for policymakers, urban planners and technology developers to design AI governance systems that are transparent, accountable and aligned with the SDGs. Integrating generative and predictive AI can enhance operational efficiency, support participatory planning and promote responsible decision-making. The findings inform the development of adaptive policy frameworks that advance SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) through digital literacy initiatives, cross-sector collaboration and data-informed management. Strengthening these practices enables cities to translate AI’s potential into tangible contributions to inclusive and sustainable urban transformation. Social implications Integrating user readiness and digital literacy into AI adoption is essential for building inclusive and trustworthy smart cities. These efforts support SDG 4 (Quality Education), SDG 10 (Reduced Inequalities) and SDG 16 (Peace, Justice and Strong Institutions). Generative AI encourages citizen participation and collaborative planning, while predictive AI improves service accessibility and data-informed governance. Promoting ethical awareness and community engagement helps narrow digital divides and address bias. Collectively, these elements advance SDG 11 (Sustainable Cities and Communities) and SDG 17 (Partnerships for the Goals) by fostering socially responsive and transparent AI-driven urban development. Originality/value This review is among the first to integrate perspectives on user readiness and technology adoption with comparative insights into generative and predictive AI in smart cities. It advances understanding of how AI-driven urban innovation supports inclusivity, efficiency and sustainability, while outlining policy directions and a future research agenda for equitable and transparent AI governance.

BackgroundOnline health information seeking is undergoing a major shift with the advent of artificial intelligence (AI)–powered technologies such as voice assistants and large language models (LLMs). While existing health information–seeking behavior models have long explained how people find and evaluate health information, less is known about how users engage with these newer tools, particularly tools that provide “one” answer rather than the resources to investigate a number of different sources.ObjectiveThis study aimed to explore how people use and perceive AI- and voice-assisted technologies when searching for health information and to evaluate whether these tools are reshaping traditional patterns of health information seeking and credibility assessment.MethodsWe conducted in-depth qualitative research with 27 participants (ages 19-80 years) using a think-aloud protocol. Participants searched for health information across 3 platforms—Google, ChatGPT, and Alexa—while verbalizing their thought processes. Prompts included both a standardized hypothetical scenario and a personally relevant health query. Sessions were transcribed and analyzed using reflexive thematic analysis to identify patterns in search behavior, perceptions of trust and utility, and differences across platforms and user demographics.ResultsParticipants integrated AI tools into their broader search routines rather than using them in isolation. ChatGPT was valued for its clarity, speed, and ability to generate keywords or summarize complex topics, even by users skeptical of its accuracy. Trust and utility did not always align; participants often used ChatGPT despite concerns about sourcing and bias. Google’s AI Overviews were met with caution—participants frequently skipped them to review traditional search results. Alexa was viewed as convenient but limited, particularly for in-depth health queries. Platform choice was influenced by the seriousness of the health issue, context of use, and prior experience. One-third of participants were multilingual, and they identified challenges with voice recognition, cultural relevance, and data provenance. Overall, users exhibited sophisticated “mix-and-match” behaviors, drawing on multiple tools depending on context, urgency, and familiarity.ConclusionsThe findings suggest the need for additional research into the ways in which search behavior in the era of AI- and voice-assisted technologies is becoming more dynamic and context-driven. While the sample size is small, participants in this study selectively engaged with AI- and voice-assisted tools based on perceived usefulness, not just trustworthiness, challenging assumptions that credibility is the primary driver of technology adoption. Findings highlight the need for digital health literacy efforts that help users evaluate both the capabilities and limitations of emerging tools. Given the rapid evolution of search technologies, longitudinal studies and real-time observation methods are essential for understanding how AI continues to reshape health information seeking.

The rapid integration of generative artificial intelligence (GenAI) into higher education has sparked debates about the future role of teachers (Chan &amp; Tsi, 2024), including in providing feedback information to students. While GenAI offers unprecedented accessibility and immediacy, this presentation argues that teachers' expertise remains irreplaceable in productive feedback – i.e., processes in which students make sense of information about their performance and use it to improve the quality of their work or learning strategies (Henderson et al., 2019, p. 1402). Drawing on a large-scale, cross-institutional survey involving 6,960 Australian university students (Henderson et al., 2025), this Pecha Kucha highlights students' perceptions of GenAI versus teacher feedback. The quantitative analysis revealed that nearly half of them (49.7%) reported using GenAI for feedback. However, they rated teacher feedback as more helpful and significantly more trustworthy. While 83.9% found GenAI feedback helpful, only 60.1% considered it trustworthy, compared to 90.5% who trusted teacher feedback. This trust gap may reflect the inconsistent quality identified in GenAI's feedback comments (Venter et al., 2024). The thematic analysis of 5,736 open-ended responses from students who used GenAI for feedback yielded 8,498 coded instances, revealing four interrelated characteristics in which teacher feedback was perceived as outperforming GenAI. Contextualisation and Relevance: Teacher feedback was perceived as more sensitive to specific assignment contexts (95.2% of 669 instances rated GenAI as less contextualised than teacher feedback) and more relevant to learning objectives (84.6% of 123 instances rated GenAI as less relevant). This contextual awareness enables teachers to identify what matters within disciplinary and course-specific frameworks. Reliability and Accuracy: Students perceived teacher feedback as significantly more reliable and trustworthy (95.4% of 1143 instances), reflecting teachers' ability to provide more trustworthy and accurate guidance without the hallucinations and factual inaccuracies that can appear on GenAI outputs. Relational Significance: Teachers offered more personal, connected feedback experiences (93.8% of 471 instances), providing the interpersonal recognition essential for productive learning relationships. This relational dimension cannot be replicated by GenAI’s algorithmic responses. Expertise: Students recognised teachers as more authoritative sources (88.2% of 119 instances), valuing their disciplinary knowledge and pedagogical understanding of student development trajectories. Students' evaluation of feedback is fundamentally shaped by perceptions of source credibility (Bearman et al., 2024), which may explain why students perceive teacher feedback as more trustworthy than GenAI's. Research demonstrates this selective engagement: uptake of content-focused GenAI feedback was considerably lower than form-focused feedback(Ziqi et al., 2024), suggesting students recognise GenAI's limitations for substantive guidance requiring disciplinary expertise. This translates into learning outcomes, with students not only perceiving instructor feedback as more useful but also demonstrating significantly greater lab score improvements than those receiving GenAI feedback (Er et al., 2025). GenAI may create opportunities for educators to focus on what they do best: providing expert, contextualised, and relationally-grounded feedback within authentic learning relationships. This potentially positions teacher expertise as increasingly valuable, with educators prioritising higher-level pedagogical responsibilities, such as developmental guidance, facilitating critical thinking, and disciplinary enculturation, while GenAI supports lower-level feedback processes, like grammar correction and initial draft review. Students appear to already recognise this distinction, trusting teachers for more substantive, transformative feedback while appreciating GenAI's supplementary role for immediate, accessible guidance.

The wide increment and integration of Artificial Intelligence (AI) into global pathway has transformed the dynamics of globalization that has offered unparalleled momentum and increased the challenges. This articles explores the multidimensional role of AI in a rapidly globalizing world, highlighting its historical development, technological transformation and significant influence on economic, social and political scenarios. This paper emphasizes how human cognitive has developed over the historical timeframe, especially with Artificial Neural Networks, replicating human logic to execute the challenging tasks. With the advent of AI from initial symbolic reasoning to present machine learning and neural networks, AI has profoundly affected the industries, jobs and overall global relations. AI has restructured the business models, improving the decision-making, boosting efficiency, which has sometimes provoked fears of automation related job displacement, data privacy issues, digital gaps and geopolitical power imbalances. Economically prosperous countries such as U.S. and China are at the frontline of AI adoption and fostering the disproportionate gains, while emerging economies are facing obstacles including limited infrastructure, skills shortages and limited access of technology. However, AI has offered the tools to resolve social dilemmas and achieve inclusive growth. This article highlights the international collaboration, ethical regulation and sustainable policy structures as it is necessary to mitigate the AI gap. Conclusively, AI is an enhancer of global change, meantime, researches urges responsible innovation to ensure its benefits are equitably shared, fostering more inclusiveness, interconnectedness and sustainable growth in the future.

In today's fast paced world, where change is accelerated and technological advancements are rapid. Ayurveda's timeless principles which prioritize health as the foundation of Dharma, Artha, Kama and Moksha must be adapted to modern realities through a strategic convergence with Artificial Intelligence. Different branches of Ayurveda also have many hidden opportunities which needs to be revealed so a comprehensive study of all the departments in integration with A.I. is a major requirement. Integrating Artificial Intelligence (AI) in Ayurveda presents a transformative approach to enhancing traditional medical practices. AI's data analysis and machine learning capabilities can optimize Ayurvedic diagnosis, treatment personalization, and predictive healthcare. By leveraging AI algorithms, Ayurveda can analyze vast patient data to identify patterns, recommend therapies, and forecast health outcomes, thus improving precision and effectiveness. Additionally, AI aids in digitizing ancient texts, making Ayurvedic knowledge more accessible. Precision medicine, a paradigm shift from the one-size-fits-all approach to personalized healthcare, aims to tailor medical treatment and interventions to individual patients based on their genetic makeup, environment, and lifestyle. With the advent of artificial intelligence (AI) technologies, particularly machine learning and deep learning algorithms, precision medicine has witnessed unprecedented advancements. The integration of artificial intelligence (AI) has significantly accelerated advancements in precision medicine, enabling the analysis of vast amounts of heterogeneous data to uncover personalized treatment options and disease mechanisms. In this paper we will discuss. "Integration of Artificial Intelligence in Precision Ayurveda Medicine "

Generative artificial intelligence (GenAI) applications, such as ChatGPT, are transforming how individuals access health information, offering conversational and highly personalized interactions. While these technologies can enhance health literacy and decision‐making, their capacity to generate deeply tailored—hypercustomized—responses risks amplifying confirmation bias by reinforcing pre‐existing beliefs, obscuring medical consensus, and perpetuating misinformation, posing significant challenges to public health. This paper examines GenAI‐mediated confirmation bias in health information seeking, driven by the interplay between GenAI's hypercustomization capabilities and users’ confirmatory tendencies. Drawing on parallels with traditional online information‐seeking behaviors, we identify three key “pressure points” where biases might emerge: query phrasing, preference for belief‐consistent content, and resistance to belief‐inconsistent information. Using illustrative examples, we highlight the limitations of existing safeguards and argue that even minor variations in applications’ configuration (e.g., Custom GPT) can exacerbate these biases along those pressure points. Given the widespread adoption and fragmentation (e.g., OpenAI's GPT Store) of GenAI applications, their influence on health‐seeking behaviors demands urgent attention. Since technical safeguards alone may be insufficient, we propose a set of interventions, including enhancing digital literacy, empowering users with critical engagement strategies, and implementing robust regulatory oversight. These recommendations aim to ensure the safe integration of GenAI into daily life, supporting informed decision‐making and preserving the integrity of public understanding of health information.

The integration of artificial intelligence (AI) into health communication systems has introduced a transformative approach to public health management, particularly during public health emergencies, capable of reaching billions through familiar digital channels. This paper explores the utility and implications of generalist conversational artificial intelligence (CAI) advanced AI systems trained on extensive datasets to handle a wide range of conversational tasks across various domains with human-like responsiveness. The specific focus is on the application of generalist CAI within messaging services, emphasizing its potential to enhance public health communication. We highlight the evolution and current applications of AI-driven messaging services, including their ability to provide personalized, scalable, and accessible health interventions. Specifically, we discuss the integration of large language models and generative AI in mainstream messaging platforms, which potentially outperform traditional information retrieval systems in public health contexts. We report a critical examination of the advantages of generalist CAI in delivering health information, with a case of its operationalization during the COVID-19 pandemic and propose the strategic deployment of these technologies in collaboration with public health agencies. In addition, we address significant challenges and ethical considerations, such as AI biases, misinformation, privacy concerns, and the required regulatory oversight. We envision a future with leverages generalist CAI in messaging apps, proposing a multiagent approach to enhance the reliability and specificity of health communications. We hope this commentary initiates the necessary conversations and research toward building evaluation approaches, adaptive strategies, and robust legal and technical frameworks to fully realize the benefits of AI-enhanced communications in public health, aiming to ensure equitable and effective health outcomes across diverse populations.