Journal of Medical Internet Research

Refugees frequently face language and access barriers to preventive oral health information. Brief multilingual digital interventions may help reduce such barriers in shelter settings.

The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision medicine and clinical decision support, the need for secure, privacy-preserving, and collaborative learning frameworks has become critical. This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics. FL enables collaborative training across distributed institutions without raw data sharing, in alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). However, FL remains vulnerable to model poisoning and gradient leakage. To address these risks, we introduce blockchain-based FL (BCFL), which leverages blockchain’s immutable ledger and decentralized consensus to enhance trust, verifiability, and auditability. The tutorial’s main contributions include (1) a taxonomy of diverse medical data types and their FL requirements; (2) three integration architectures (fully coupled, semicoupled, and loosely coupled) analyzed for security, scalability, and regulatory compliance; (3) a security analysis of health care–specific vulnerabilities and mitigation strategies using advanced cryptography, such as zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and United States Food and Drug Administration guidelines for AI-enabled medical devices. We demonstrate BCFL’s relevance across major health care applications, including disease prediction, medical imaging, patient monitoring, and drug discovery, and highlight emerging research directions such as quantum-resilient cryptography, scalable interoperability, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, and collaborative AI in health care; fostering privacy-preserving analytics; and improving patient outcomes.

Online health communities (OHCs) have emerged as critical platforms for patients with type 1 diabetes (T1D) to exchange informational and emotional support. However, how stakeholder roles and disease duration jointly shape support dynamics and influence formation remains underexplored.

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide. Retinal imaging and deep learning (DL) may support scalable screening, but deployment requires evidence on pooled performance. This is important because missed neovascular disease may delay treatment, whereas excessive false positives may overload referral pathways.

The integration of large language models (LLMs) into medicine has reshaped health care delivery, education, and research. Although proprietary models face challenges such as data privacy, regulation, and adaptability, DeepSeek, an open-source LLM, has emerged as a customizable and cost-effective alternative with significant potential for clinical and operational applications. However, the rapid expansion of research in this area necessitates a systematic mapping of its landscape, applications, and challenges.

The digitization of medical data and advances in interoperability have opened opportunities for research studies to use more comprehensive, longitudinal patient data from multiple sources. As patients often interact with many providers and payers over time, collecting data across organizations may have critical implications for accuracy and bias in study results. US policy has promoted exchanging health information among providers, payers, and patients, but less attention has focused on facilitating data collection for research, which presents unique challenges.

Online scheduling platforms are increasingly chosen by patients for scheduling outpatient appointments. Due to payment for listing or platform decisions on listing visibility, they can amplify access inequalities. Especially in Germany’s dual insurance system, the beneficiary difference in waiting times for private health insurance (PHI) patients compared to statutory health insurance (SHI) patients for specialist appointments might increase.

As artificial intelligence (AI) models become increasingly integrated into facial aesthetic surgery for attractiveness prediction and surgical outcome simulation, their potential to perpetuate bias poses clinical concerns. Current models trained on limited datasets inaccurately evaluate underrepresented populations and risk promoting aesthetic homogenization that conflicts with patient goals of ethnic feature preservation. Drawing on current literature, this paper examines bias across AI development stages in aesthetic facial evaluation. Benchmark datasets such as SCUT-FBP (South China University of Technology—Facial Beauty Prediction) and the Chicago Face Database underrepresent older adults, non-White, and ethnically diverse populations. Training methodologies lack fairness-aware techniques, and evaluation focuses on overall rather than demographic-stratified accuracy. While individual mitigation strategies exist—including balanced datasets, adversarial debiasing, and fairness metrics—no comprehensive framework integrates these approaches across the entire development lifecycle. We propose a 6-pillar framework spanning the AI development lifecycle: (1) diverse data collection with synthetic augmentation, (2) fairness-aware training techniques, (3) complementary fairness metrics with intersectional assessment, (4) explainable AI for clinical transparency, (5) stakeholder engagement, and (6) continuous monitoring. Despite the challenges of maintaining algorithmic standardization and cultural specificity, this framework provides implementation guidance for AI developers, clinicians, and institutions, with principles applicable beyond aesthetic surgery to broader facial analysis applications.

Persons living with HIV have an increased risk of cardiovascular and metabolic diseases compared with HIV-seronegative individuals.

Understanding complex health information, such as kidney function values (eg, creatinine), is important for youth kidney transplant recipients and caregivers to effectively engage and participate in their care. Information visualizations, such as visual analogies, highlight the similarities between 2 different ideas through visual means and can support understanding of abstract data to facilitate self-management. The study was motivated by the persistent challenge that youth and caregivers face in interpreting complex clinical data, which often remains unactionable and disconnected from their practical information needs.