Journal of Medical Internet Research

Preeclampsia is a severe hypertensive disorder with rising global prevalence. While machine learning (ML) models for predicting preeclampsia are increasingly published, existing evidence shows high heterogeneity, and the distinction between internal performance and external transferability remains unclear.

Effective communication is fundamental to health care; however, demographic transitions and a widening global health workforce gap are intensifying the imbalance between service demand and resource supply. Health conversational artificial intelligence (HCAI) based on large language models offers a potential pathway to improve the accessibility and personalization of care. Nevertheless, the lack of a rigorous, user-centered evaluation framework limits the systematic assessment of HCAI quality, raising concerns regarding safety, reliability, and clinical applicability.

Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic disease, and health education is vital to disease management and rehabilitation. The emergence of large language models (LLMs) has provided new opportunities for health education. However, the effectiveness and applicability of LLMs in education with DDH have not been systematically evaluated.

Osteoporosis (OP) is projected to be a major issue significantly impacting the well-being of middle-aged and old populations. Machine learning (ML) and deep learning (DL) models developed based on medical imaging have enhanced clinicians’ diagnostic accuracy and work efficiency. However, the diagnostic performance of different types of medical imaging for OP has not been systematically assessed.

Adolescence is a critical period for mental health vulnerability alongside rising digital media exposure. Current evidence often fails to distinguish the distinct roles of leisure screen time (LST) quantity and addictive patterns like internet gaming disorder (IGD) on a comprehensive range of mental health outcomes.

Digital health services are increasingly used in hospital-based outpatient care, offering remote monitoring, patient-reported outcomes, information sharing, and asynchronous communication. While expected to improve self-management, timeliness, and efficiency, the success of digital health interventions relies on patients’ health literacy and digital health literacy. While some research has addressed potential associations between digital health interventions and patients’ health outcomes, research on patients’ experiences remains limited.

The rapid growth of Internet Healthcare (IH) offers the older adults convenient medical services like remote consultations and health monitoring. However, its adoption among this group remains low, highlighting a significant digital divide. Understanding the behavioral patterns and determinants of IH use in the older population is crucial for optimizing digital health design and improving service accessibility.

The advent of tirzepatide has transformed obesity care; yet, real-world weight loss outcomes necessarily depend on patient engagement with behavioral support. Digital platforms offering coaching, self-monitoring, and automated feedback have the potential to further augment pharmacological efficacy.

The digital health research field is growing rapidly, and a summary of the available digital tools for triaging musculoskeletal conditions is needed. Effective and safe digital triage tools for musculoskeletal conditions could support patients in making informed care decisions, aid clinicians and patients in navigating care, and may contribute to reducing ED overcrowding and healthcare costs.

Large language models are rapidly transitioning from pilot schemes to routine clinical practice. This creates an urgent need for clinicians to develop the necessary skills to strike the right balance between seizing opportunities and taking accountability. We propose a 3-tier competency framework to support clinicians’ evolution from cautious users to responsible stewards of artificial intelligence (AI). Tier 1 (foundational skills) defines the minimum competencies for safe use, including prompt engineering, human–AI agent interaction, security and privacy awareness, and the clinician-patient interface (transparency and consent). Tier 2 (intermediate skills) emphasizes evaluative expertise, including bias detection and mitigation, interpretation of explainability outputs, and the effective clinical integration of AI-generated workflows. Tier 3 (advanced skills) establishes leadership capabilities, mandating competencies in ethical governance (delineating accountability and liability boundaries), regulatory strategy, and model life cycle management—specifically, the ability to govern algorithmic adaptation and change protocols. Integrating this framework into continuing medical education programs and role-specific job descriptions could enhance clinicians’ ability to use AI safely and responsibly. This could standardize deployment and support safer clinical practice, with the potential to improve patient outcomes.

Cardiac exercise rehabilitation is an important intervention for disease management of patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI). Still, the participation and compliance with exercise rehabilitation remain suboptimal. Mobile health technology is a promising approach to promoting involvement in cardiac exercise rehabilitation. Remote rehabilitation can overcome the problems existing in traditional rehabilitation.

Vessels encapsulating tumor clusters (VETC) are significantly associated with poor prognosis in hepatocellular carcinoma (HCC). However, identifying VETC early remains challenging. Recently, machine learning has shown promise for VETC detection, but their diagnostic accuracy lacks systematic validation.