Journal of Medical Internet Research

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.

Digital overuse poses a significant threat to health through multiple pathways, including the deterioration of mental health and sleep disturbance. This issue has led to the development of digital overuse-targeted digital behavior change interventions (DO-DBCIs). Although research in this field has advanced by adopting state-of-the-art technologies, substantial gaps exist in elements critical to establishing intervention validity, including target devices and activities, intervention strategies, theoretical foundations, target populations, and sustainability of effects.

Suicide is a major public health challenge. Traditional assessments of suicidal thoughts and behaviors rely on retrospective measures that are subject to recall bias and show limited predictive accuracy. Ecological momentary assessment (EMA) can improve suicide risk assessment by capturing real-time information in participants’ natural environments. However, EMA burden often results in short follow-up periods.

Anticoagulants are high-alert medications with substantial risk of serious bleeding, yet dosing and prescribing errors remain common. Although clinical decision support systems (CDSSs) can mitigate these errors, their impact is constrained by alert fatigue and limited interpretability.

Traditional laboratory-confirmed influenza surveillance involves a 1- to 2-week reporting delay and captures only patients who have sought care and received a diagnosis, limiting early warning. Digital prescription data have been shown to signal influenza activity early, but their predictive value for forecasting remains unclear.

AI is increasingly embedded in digital public health systems, but model performance, system deployment, message delivery, alert volume, and user engagement do not establish whether an AI-supported workflow has changed public health practice. This viewpoint proposes a public health action end point—a prespecified, auditable, AI output–specific, actor-bound, time-bound, denominator-based way to specify and measure an existing proximal process or implementation end point. It begins with a defined AI output and records the corresponding action opportunity, accountable actor, action status, and evidence concerning the AI’s role in the action. A complete specification includes rules for repeated outputs, completed action, justified nonaction, missed action, unresolved cases, denominator loss, and paired safety, workload, privacy, equity, and model drift monitoring. A public health action end point is not a new causal outcome, reporting guideline, or validated surrogate for a population health benefit. An observed action rate alone does not establish that AI initiated or caused the action; causal claims require a documented attribution strategy and an appropriate comparator or causal design. The framework applies to research and production deployments but complements rather than replaces ethics review, trial registration, and jurisdiction-specific regulatory and governance obligations. It is intended to help authors, implementers, reviewers, and editors align claims with what an evaluation has actually measured.

This research letter summarizes the development and deployment of a data analytics dashboard that uses a simple rule-based keyword search to streamline pre–magnetic resonance imaging (MRI) safety screening for implantable medical devices, resulting in a 98% reduction in the manual screening workload while maintaining strong performance metrics.