Journal of Medical Internet Research

Language barriers between health care providers and patients can compromise communication quality, patient safety, and health care equity. When professional interpreter services are limited, particularly in outpatient settings, artificial intelligence–based translation tools may serve as supplementary communication aids.

Promoting early HIV testing and patient detection is an important public health goal. In Japan, approximately 30% of the population is diagnosed with AIDS. Several studies have investigated the challenges related to HIV diagnosis; however, there are limitations in understanding the characteristics and barriers faced by individuals who are at high risk of HIV but have not yet been tested or have not sought medical consultation.

Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizations and hinder technological progress in public health.

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Ecological momentary interventions (EMIs) may be a promising tool for promoting mental well-being in youth, as they allow for targeting resilience and other protective factors in daily life.

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This article argues that despite the remarkable advances of artificial intelligence (AI) in medicine —including demonstrated capabilities in image recognition, diagnosis, treatment planning, and even empathic communication in controlled settings—the core of medical practice remains irreducibly human. We identify three domains in which AI cannot replace doctors: the holistic, sensory art of clinical observation and intuition; the longitudinal, trust-based doctor-patient relationship built on genuine emotional connection; and the capacity to embrace clinical uncertainty, exercise moral responsibility, and make courageous decisions in the absence of algorithmic guidance. The intended audience includes clinical doctors, medical students, medical educators, and health policy makers navigating the integration of AI into practice. We conclude that preserving “AI-free clinical time” in medical training and safeguarding the humanistic dimensions of care are essential, and technology is to complement rather than diminish the healing arts.

Digital health technologies (DHTs) are increasingly integrated into clinical practice, yet economic evaluations remain scarce, particularly in the early development stages. Within the NICE (National Institute for Health and Care Excellence) Evidence Standards Framework, Tier C DHTs comprise technologies with direct clinical implications and measurable health outcomes, for which robust economic evidence is essential. Early-stage assessments are particularly important to inform subsequent development, refinement, and adoption decisions across the digital health lifecycle.

Preoperative cardiovascular risk stratification is essential in noncardiac surgery, but conventional testing is frequently overused, increasing costs without improving outcomes. Artificial intelligence (AI)–enabled electrocardiography (ECG) may enhance perioperative risk assessment by identifying surgical candidates at very low-risk for adverse events.

To be beneficial for empirical health research, a dataset must be fit for use. The quality of a dataset can only be influenced during data collection, yet it is evaluated multiple times during analysis or secondary use by applying quality indicators.

While cross-sectional studies have consistently reported an association between nonsuicidal self-injury (NSSI) and internet addiction (IA), longitudinal evidence regarding the directionality and dose-response relationship remains limited. Furthermore, the roles of sex and varying degrees of problematic internet use in predicting new-onset NSSI are not fully understood.