Journal of Medical Internet Research

The internet has become a major source of health information; yet, the quality of health information on websites varies considerably. Users’ ability to evaluate either the factual accuracy or the trustworthiness of health information on websites is limited, as around half of the European people have limited health literacy. Existing checklists and tools are either prepared for research purposes or to be used by health care professionals. They do not account for the lay user perspective, since they are too long and complicated to be used by laypersons, or were developed for printed health information only.

Information on menopause can come from a variety of sources, from friends and family to health care professionals, but increasingly, digital information has become a significant source. Digital information can be accessed online, through websites, social media, podcasts, and online groups or forums. The extent to which digital information on menopause is accessed and consumed can vary widely depending on individual circumstances. Existing literature has focused on investigating a single technology at a time in terms of digital menopause information, rather than exploring a wider ecosystem that people may use. As a result, existing evidence is perhaps limited in the understanding of the role these internet-enabled digital technologies play holistically, specifically for digital menopause information.

Chronic disease management increasingly relies on digital health information. Traditional adoption models conceptualize adoption as an individual decision determined by cognitive evaluations. However, in contemporary platform environments, health information exposure and interaction are shaped by algorithmic curation and platform governance. Understanding how patients with chronic disease engage with these platform-mediated spaces requires examining adoption as an ongoing process rather than a discrete outcome. Such process-oriented understanding remains limited.

Human papillomavirus (HPV) is a sexually transmitted virus that causes various oropharyngeal and anogenital cancers. The HPV vaccine provides protection against several strains of HPV and is a key preventative tool against HPV-related cancers; however, vaccination rates remain suboptimal in the United States due to variable state mandates and misperceptions of vaccine efficacy and risks. As social media becomes an increasingly popular avenue for health discussions, platforms such as Reddit offer opportunities to understand public vaccine discourse, particularly among underrepresented groups. Furthermore, vaccine hesitancy and mistrust generally increased during the COVID-19 pandemic, potentially impacting HPV vaccination rates.

Multiple long-term conditions (MLTCs) require complex and prolonged treatment regimens. Remission in long-term conditions (LTCs) is important for understanding disease progression and evaluating treatment effectiveness. Electronic health records (EHRs) are increasingly used to monitor clinical outcomes, but how remission is defined within EHRs remains unclear.

Digital health technologies are increasingly used to monitor and improve personal health and well-being. Simultaneously, they can influence user behavior and self-understanding. As health technologies advance and are embedded in everyday life, understanding their broader psychological impacts, as well as the role of identity in shaping these outcomes, is crucial.

Completion rates for smoking cessation treatments under Japan’s national health insurance system remain suboptimal. A workplace cessation program, combining nicotine gum or patches with a group-based digital peer-supported app, has reported high cessation success rates. Although nicotine dependence is generally associated with lower cessation success, and self-confidence is generally associated with higher success, these associations may differ by tobacco product type. Evidence on these relationships in app-based cessation programs remains limited.

Online health information–seeking behavior (OHISB) has become an increasingly common component of contemporary health self-management. Individuals use a wide range of digital sources, including websites, social media platforms, and mobile apps, to obtain health-related information. However, substantial disparities persist in who seeks health information online, and which populations benefit from digital health resources. While previous research has largely focused on individual-level determinants, cross-national evidence on structural influences remains limited.

Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence—systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle–oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world’s first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it—at the speed at which adaptive intelligence actually changes.

Concealment of psychiatric symptoms is a barrier to effective mental health treatment, particularly among patients with suicidal thoughts and behaviors. Prior research on concealment has relied on retrospective self-report or laboratory-based interviews, which may not capture real-world decision-making about disclosure. Social media platforms such as TikTok provide a context in which individuals publicly narrate their experiences about concealing psychiatric symptoms, offering insight into motivations for concealment uninfluenced by experimenter demand characteristics.

Poststroke dysarthria, a common speech impairment, affects up to half of all stroke survivors, often reducing their ability to communicate, and adversely affecting their quality of life. Although conventional speech therapy for poststroke dysarthria is effective, access is often limited by time and geographical constraints. Here, digital speech therapy may serve as a remotely deliverable alternative for selected patients. However, few trials have assessed its efficacy, safety, and usability.

The global burden of obesity continues to rise, highlighting the need for patient-centered approaches to weight management. Shared decision-making is particularly important in the selection of antiobesity medications (AOMs), as treatment options differ in mechanism, effectiveness, side effects, routes of administration, and cost. Despite this preference-sensitive context, only a few patient decision aids (PDAs) have been culturally and clinically adapted for use in Asian populations.