Journal of Medical Internet Research

The traditional method for diagnosing obstructive sleep apnea (OSA) through polysomnography may be expensive and inaccessible. Recent developments in AI propose the use of photoplethysmography (PPG) to aid OSA diagnosis.

Large language models (LLMs) have the potential to improve the efficiency of evidence synthesis, but their reliability in performing complex tasks such as risk-of-bias (ROB) assessment in randomized controlled trials (RCTs) remains unclear.

Digital health interventions (DHIs) offer scalable ways to reach youth. While DHI research often poses minimal risk, guardian permission is typically required for minors to participate. As a result, requiring guardian permission may unintentionally obstruct safe, high-quality research and delay its translation.

Pediatric health care requires distinct considerations, including caregiver involvement and developmental differences in cognition and communication as children gain autonomy, particularly as pediatric health care chatbots gradually emerge. Because childhood and adolescence are formative periods for health behaviors and self-management practices, pediatric chatbots also warrant evaluation against long-term rather than immediate outcomes.

Digital health technologies increasingly support long-term stroke management and rehabilitation, but technophobia may hinder their acceptance and sustained use among older patients with stroke. Evidence in this population remains limited.

Real-world gait assessment has gained momentum in populations with walking impairments, offering insights beyond standardized tests and supporting the integration of remote monitoring into clinical care. However, its potential remains limited by the lack of validated population- and context-specific digital biomarkers.

Emergency triage of anterior circulation large vessel occlusion (LVO) is constrained by delays in vascular imaging, specialist interpretation, and transfer decision-making. Noncontrast computed tomography (NCCT) is often obtained first in suspected stroke, but visual recognition of LVO in NCCT images is difficult outside specialist settings. NCCT-based AI may provide an early human-in-the-loop escalation signal.

Physician documentation requirements are a known contributor to clinician burnout, with the manual creation of brief hospital course (BHC) summaries being particularly time-consuming. Automating BHC summarization may mitigate this workload and reduce documentation errors. However, current natural language processing (NLP) methods are often limited to single-document inputs, and large language models (LLMs) face privacy and deployment challenges. Furthermore, existing methods often require manual information extraction and struggle to maintain temporal accuracy.

Globally, health systems face increasing challenges due to improved life expectancy, rising levels of disease, greater expectations for health care, and growing expenditure. AI-based technologies are increasingly used to support clinical decision-making and contribute to effective delivery of care. AI has significant potential in terms of diagnostics and treatment for preventive and personalized medicine, including progressive, nonprogressive, or neurodivergent conditions affecting the brain, spinal cord, and nerves. Although AI has been extensively applied to several areas of health, its application to neurological conditions remains limited.

Digital health technologies offer promising opportunities to support physical health. However, their acceptance, use, and associated benefits are not equally distributed across society. While existing research has mainly focused on traditional socioeconomic indicators, broader sociological influences, including economic, cultural, social, and person capital, may provide a more comprehensive understanding of these inequalities. Yet, too little is currently known about how different subgroups, based on their economic, cultural, social, and person capital, relate to intentions to accept and use digital health technologies.

This article is a viewpoint: it presents the authors’ perspective, informed by a critical synthesis of the current literature at the intersection of generative AI (GenAI) technologies, public health communication, and digital ethics, rather than original empirical data or analyses. The emergence of GenAI, including large language models (LLMs), represents a profound paradigm shift in digital health communication. By moving beyond traditional information retrieval to dynamic, human-like knowledge generation, GenAI offers unprecedented opportunities for public health promotion. However, the unguided integration of these powerful commercial models into health care systems poses profound sociotechnical risks. In this viewpoint, we aim to communicate three key messages to public health researchers, practitioners, policymakers, and AI developers: (1) GenAI offers transformative applications for public health promotion, spanning personalized health education, stigma mitigation, and accelerated epidemiological surveillance; (2) the unguided integration of commercial generative models simultaneously generates intersecting sociotechnical risks and ethical challenges, encompassing a widening “AI digital divide,” algorithmic bias and epistemic opacity, and the erosion of data privacy and governance; and (3) an equity-centered sociotechnical architecture, built on 4 strategic pillars, is required to govern this transition safely. We conducted a critical synthesis of the current literature and theoretical frameworks at the intersection of GenAI technologies, public health communication, and digital ethics, systematically mapping both the translational capabilities and the sociotechnical vulnerabilities of generative models. GenAI demonstrates transformative potential across 3 primary domains: democratizing health education by translating complex medical jargon, mitigating societal stigma through nonjudgmental conversational interfaces, and accelerating epidemiological surveillance via rapid thematic synthesis. However, these benefits are counterbalanced by a matrix of sociotechnical risks. Specifically, unguided GenAI deployment threatens to exacerbate a novel “AI digital divide” driven by economic exclusion, prompt literacy demands, and linguistic biases; compromise clinical safety through deep-seated algorithmic biases and epistemic opacity; and erode patient privacy through profound vulnerabilities in cybersecurity and corporate data governance. The advent of GenAI marked an irreversible paradigm shift with the unprecedented capacity to democratize health literacy, dismantle stigma, and accelerate disease surveillance. However, treating GenAI as a technological panacea is a perilous oversight. Without intentional, equity-focused interventions, these technologies invariably scale and automate the structural inequalities they have the potential to solve. Ultimately, the future of digital health promotion depends not only on the computational power of these models but also on the ethical, regulatory, and inclusive sociotechnical architectures we design to govern them.

Failure to recognize clinical deterioration in hospitalized patients has prompted the development of rule-based electronic surveillance (RB-ES), predictive model–based electronic surveillance (PM-ES), and continuous physiologic monitoring (CPM). However, their comparative effects on patient-centered outcomes remain uncertain.