Journal of Medical Internet Research

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.

Digital innovations hold immense potential to transform health care delivery, particularly in sub-Saharan Africa, where financial, geographical, and infrastructural constraints continue to hinder progress toward universal health care delivery. Although a growing health tech sector offers creative solutions, few digital health interventions reach scaled implementation. In this paper, we present the digital fit/viability model—an adapted determinant framework to describe facilitators and barriers to moving from digital tools to integrated digital health implementation. We then use this model to describe the specific challenges and recommended solutions when developing digital health tools for health systems in sub-Saharan Africa.

Diet-related Health Recommender Systems (HRS) have gained attention for their potential to provide personalized dietary guidance, particularly for patients with chronic conditions. However, studies on diet-related HRS in healthcare are relatively limited.

Chronic obstructive pulmonary disease (COPD) is a common chronic lung disease. Deep learning (DL), a data-driven machine learning approach, has gained attention in clinical practice, particularly for diagnosing COPD and grading its severity. However, systematic evidence of its diagnostic and grading accuracy remains limited, posing challenges for developing intelligent diagnostic tools.

Hypertension remains a major global health challenge, significantly increasing cardiovascular and all-cause mortality risks. While exercise therapy is effective, conventional approaches face limitations in accessibility and personalization, compromising adherence. Artificial intelligence (AI)–assisted remote rehabilitation enables real-time monitoring and personalized guidance, offering a promising alternative. Nevertheless, its clinical benefits and applicability require further systematic validation.

The global rise of metabolic associated fatty liver disease (MAFLD) reflects the urgent need for accurate, non-invasive diagnostic approaches. The invasive nature of liver biopsy and the limited sensitivity of ultrasound (US) in detecting early steatosis highlight a critical diagnostic gap. Artificial intelligence (AI) has emerged as a transformative tool, enabling the automated detection and grading of hepatic steatosis (HS) from medical imaging data.

Artificial Intelligence (AI)-enabled devices are increasingly used in healthcare. However, there has been limited research on patients’ informational preferences, including which elements of AI device labeling enhance patient understanding, trust, and acceptance. Clear and effective patient-facing communication is essential to address patient concerns and support informed decision-making regarding AI-enabled care.

In today’s digital era, the internet plays a pervasive role in our lives, influencing everyday activities such as communication, work, and leisure. This online engagement intertwines with offline experiences, shaping individuals’ overall well-being. Despite its significance, existing research often falls short in capturing the relationship between internet use and wellbeing, relying primarily on isolated studies and self-reported data. One major contributor to deteriorated wellbeing is stress. While some research has examined the relationship between internet use and stress, both positive and negative associations have been reported.

Whether the benefits of identical physical activity (PA) patterns for adults with high blood pressure vary according to an individual’s characteristics has not been adequately studied.