Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer new opportunities for both the public and professionals. However, these advancements also pose risks of exacerbating health disparities. Limited research has focused on combining these health communication mediums, particularly those enabled by new technologies like GenAI, and their applications for health promotion and health disparities. Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview of health communication mediums and their role in understanding health promotion and disparities in the GenAI era. Additionally, health promotion and health disparities associated with GenAI are briefly discussed through the lens of the Technology Acceptance Model 2, the uses and gratifications theory, and the knowledge gap hypothesis. This viewpoint discusses the limitations and barriers of previous internet-based communication mediums regarding real-time responses, personalized advice, and follow-up inquiries, highlighting the potential of new technology for public health promotion. It also discusses the health disparities caused by the limitations of GenAI, such as individuals’ inability to evaluate information, restricted access to services, and the lack of skill development. Overall, this study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities. It underscores the need for more empirical studies, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations.

Bangladesh witnessed its biggest nationwide mass uprising since gaining independence in 1971, which led to the overthrow of an authoritarian government that had existed for a decade. This study employed the protest paradigm to analyze how the protests were framed by mainstream print media and how media ownership influenced their coverage. Drawing on a quantitative content analysis of five major newspapers from different ownerships, the study explores dominant media frames, tone, and legitimacy of protest coverage. The findings indicate that media ownership significantly affects the credibility and tone of the protest coverage. The protest paradigm was applied more strictly by pro-government media outlets. Independent and anti-government outlets, on the other hand, took a more impartial stance. The study reveals how media ownership shaped the framing of dissent, reinforcing the protest paradigm in ways that aligned with the ruling party’s interests. This study adds to the body of knowledge on South Asian media bias and authoritarian information control.

Women's health in Bangladesh faces risks due to an alarming rise in cesarean section (CS) rates, exceeding 72% in hospital-based deliveries, far surpassing the WHO's recommended limit of 15%. This study, guided by the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB), explored socio-cultural factors influencing childbirth mode decisions. Among 503 survey participants, 91% of CS cases occurred against initial preferences, revealing a disconnect between health beliefs and behavior. Subjective norms, particularly family influence and social expectations, emerged as more critical in shaping CS decisions than physician recommendations.

Large language model (LLM)-based chatbots such as ChatGPT, Gemini, Copilot, and Perplexity are transforming healthcare communication by delivering real-time conversational responses. While their use is expanding across clinical and nonclinical settings, concerns remain regarding their accuracy, safety, and potential to exacerbate existing health inequities. This commentary synthesizes emerging research to examine the applications, limitations, and risks associated with conversational AI in healthcare, focusing particularly on underserved populations with limited digital or health literacy. These tools are increasingly used in clinical settings for tasks like triage, surgical support, and diagnostic support, and in nonclinical roles such as health education and translation. However, chatbots often produce inaccurate, generalized, or biased responses due to their reliance on user-generated prompts and publicly available training data. These limitations disproportionately impact vulnerable populations, further deepening digital and health inequities. Addressing these issues requires inclusive chatbot design, content regulation, and public education on prompt literacy to ensure equitable healthcare communication.

UNSTRUCTURED Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer new opportunities for both the public and professionals. However, these advancements also pose risks of exacerbating health disparities. Limited research has focused on combining these health communication mediums, particularly those enabled by new technologies like GenAI, and their applications for health promotion and health disparities. Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview of health communication mediums and their role in understanding health promotion and disparities in the GenAI era. Additionally, health promotion and health disparities associated with GenAI are briefly discussed through the lens of the Technology Acceptance Model 2, the uses and gratifications theory, and the knowledge gap hypothesis. This viewpoint discusses the limitations and barriers of previous internet-based communication mediums regarding real-time responses, personalized advice, and follow-up inquiries, highlighting the potential of new technology for public health promotion. It also discusses the health disparities caused by the limitations of GenAI, such as individuals’ inability to evaluate information, restricted access to services, and the lack of skill development. Overall, this study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities. It underscores the need for more empirical studies, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations.