Journal of Medical Internet Research

In health systems, AI is increasingly being implemented as a form of clinical decision support (CDS). Research on patient perspectives, however, lags behind clinician-focused studies. Before the introduction of AI-based tools, clinicians often used and continue to use rule-based guidelines and risk calculators (RCs) to inform decision-making. Prior research has shown that RCs for CDS have mixed effects on patient adherence to treatment recommendations, whereas for AI-based tools, the effect is still not clear.

Clinical AI governance has advanced rapidly in technical validation, regulatory compliance, risk management, human oversight, and postdeployment monitoring. Yet a more fundamental question often remains implicit: under what conditions are health institutions justified in exercising consequential authority through AI over patients, clinicians, and communities? In this Viewpoint, we argue that legitimacy should be recognized as an independent governance requirement, distinct from technical assurance, legal and ethical compliance, trust, acceptance, and institutional approval. Our aim is to distinguish institutional approval from public authorization and to propose a normative framework for authorizing consequential clinical AI deployments. We address health system leaders, clinical AI governance and procurement teams, frontline clinicians, and patient and community representatives. Drawing on theories of legitimacy and public value and published clinical AI governance research, we make the following contributions: (1) the concept of the unclaimed decision, defined as a consequential choice about purpose, threshold, population, or priority for which no actor clearly assumes responsibility for public justification; (2) a tripartite framework comprising procedural authorization, substantive justification, and relational renewal; and (3) practical governance tools to support public justification, contestation, and renewal. These tools include 3 minimum auditable conditions: visibility of deployment; affected-party input before governing conditions are fixed; and conditional reviewability, which requires a predefined trigger that can be invoked by a named actor outside the deploying team. A 5-question public authorization test examines purpose, distribution, exclusion, meaningful choice and contestability, and renewal. Governance intensity should increase with clinical consequence; distributive reach; difficulty of reversal; and the absence of genuine, informed choice. For smaller health care organizations, we propose ways to perform the same core governance functions through existing advisory bodies, targeted interviews, shared regional resources, and standardized documentation. We also outline a heuristic for identifying affected publics, procedures for specifying participation authority and resolving disagreement, and procurement provisions that preserve reviewability across vendor boundaries. These are normative proposals requiring evaluation. Public authorization requires continuing substantive justification; it cannot be inferred from compliance, popularity, or completion of an audit checklist.

Female university students encounter appearance ideals, comparison cues, and appearance-related pressure across traditional and digital media environments. These sociocultural influences may coexist with psychological resources such as body appreciation and weight-control self-efficacy. However, variable-centered analyses may obscure heterogeneity in how these characteristics are combined within individuals. The measures used in this study assessed broader sociocultural appearance-related attitudes rather than platform-specific digital exposure.

Bipolar disorder (BP) affects 1% to 3% of adults worldwide, with diagnostic delays averaging 6 years and high initial misdiagnosis rates. The absence of objective diagnostic measures contributes to clinical uncertainty and suboptimal outcomes.

The human-horse bond has played a role in human development throughout history and has more recently been applied to mental health promotion through equine-assisted learning (EAL) programs. In EAL settings, therapeutic documentation and assessment commonly rely on practitioner observations documented through behavioral checklists, structured interviews, and progress notes, which can pose challenges due to time demands, subjectivity, and inconsistent standardization across practitioners and contexts.

Parkinson disease (PD) impairs mobility and motor function, making accurate mobility assessment important for effective management and treatment planning. Traditional clinical assessments often rely on periodic clinical evaluations, which may not capture day-to-day symptom fluctuations. Wearable devices offer a promising alternative with continuous, objective tracking of mobility (eg, gait). However, although these tools provide valuable insights for patients and clinicians, their successful implementation depends on their acceptability to users.

Parental involvement in obesity interventions is considered essential for sustainable behavior change in children and adolescents with overweight or obesity; yet, the effectiveness of parent-involved digital health interventions (DHIs) and the influence of different delivery modalities remain unclear.

Large language models (LLMs) are increasingly being used for text-based emotional support. However, general-purpose LLMs may not consistently recognize emotional cues, adapt supportive strategies to contexts, or generate responses aligned with psychologically informed communication. The limited availability of large-scale, structured Chinese counseling data further constrains the development of models for context-sensitive emotional support.

AI is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming. Public health aims to protect and improve the health of communities and populations at the local, regional, national, and global levels, but it typically has limited resources. While AI may lead to improved efficiency or effectiveness of public health interventions, the adoption of AI interventions should be informed by evidence of effective use in practice.

Addressing the burnout epidemic among health care providers remains one of the foremost challenges of modern health care systems. Past researchers have turned to passively gathered biometric data from wearable devices to identify key variables that predict burnout. However, this has resulted in limited success.

Digital health interventions, including mobile health apps, increasingly complement outpatient mental health care. In Germany, certified digital health applications (DiGA [Digitale Gesundheitsanwendungen]) can be prescribed and reimbursed in the statutory health care system. However, real-world evidence on patient-reported use of DiGA for mental disorders remains limited.