Journal of Medical Internet Research

Large language models (LLMs) demonstrate strong performance on medical knowledge benchmarks, but their safe and effective use in clinical practice depends on posttraining adaptation rather than raw model capability. Fine-tuning, retrieval-augmented generation (RAG), and hybrid approaches are principal strategies for grounding language models in clinical evidence, yet their comparative effectiveness remains unclear.

Digital technologies are increasingly integrated across health care systems; however, accumulating evidence shows that these tools often perform unevenly across population groups. Biases in AI-enabled tools can reinforce existing health inequities, particularly when systems are developed and validated using datasets that exclude underserved populations. Studies demonstrate systematic underestimation of illness severity, diagnostic inaccuracies, and measurement bias affecting ethnic minority groups, rural populations, people with disabilities, people experiencing homelessness, and other groups with limited access to health care. These disparities highlight structural vulnerabilities in the data that inform machine learning systems, where socially patterned access to care shapes what is recorded and therefore learned. Commercial adoption patterns further exacerbate inequities due to socioeconomic gradients in digital engagement and a lack of accountability measures for adherence to regulatory frameworks within National Health Service procurement processes. In this Viewpoint, we argue that ensuring accountability in digital health care requires transparent reporting of subgroup performance, representative development of datasets, and ongoing monitoring of distributional impacts. Achieving equitable and reliable digital health care demands regulatory and methodological standards that prioritize fairness and generalizability within the commercialization sector and are monitored across the pre- and postdeployment life cycle.

AI has the potential to support chest X-ray (CXR) triage in primary care, but adoption depends on whether clinicians perceive its outputs as credible, useful, and workable within routine clinical workflows. Evidence on how primary care doctors experience AI-supported CXR triage in real-world practice remains limited.

Hepatopancreatobiliary (HPB) malignancies require complex treatment planning that often relies on multidisciplinary team (MDT) discussions. Large language models (LLMs) have recently been explored for clinical decision support, but their performance within real-world multidisciplinary decision environments remains unclear. In particular, the stability of LLM-generated recommendations—that is, whether a model produces the same answer when given the same clinical input—has rarely been examined.

Machine learning (ML) and deep learning (DL) models have been developed for earlier recognition in hospitalized patients, but reported performance varies across datasets, prediction windows, care settings, and validation designs. Interpretation of a single pooled discrimination estimate is therefore uncertain, particularly because public datasets are often reused, and most evidence is retrospective.

Individuals with opioid use disorder (OUD) who return to the community from incarceration face a high risk of fatal drug overdose. This mortality risk is related to significant barriers to accessing health care, social support, and resources needed to transition to and thrive within the community safely. Digital health tools have effectively supported substance use treatment and recovery, but their potential to support OUD during the high-risk period of reentry is underexplored.

Digital health technologies are increasingly promoted as mechanisms to improve care coordination, enhance access, and reduce health care costs. However, whether community-level digitalization is associated with lower primary care spending in Medicare-participating safety net settings remains unclear, particularly in federally qualified health centers (FQHCs) and rural health clinics (RHCs).

Virtual agents powered by dialogue trees have emerged as promising tools to overcome limitations of text-only psychological interventions in mental health treatment.

Recovery from opioid use disorder (OUD) is a complex, nonlinear process involving substantial health, psychological, and social challenges. Although online social support has been shown to benefit individuals with OUD, less is known about how recovery stages, such as the initial or stable stages, are expressed and experienced in online communities. Specifically, the linguistic features characterizing each stage, the social support exchanged at each stage, and the feasibility of predicting these stage transitions from user-generated content remain largely unexplored.

The impact of biofeedback-based virtual reality (VR-BF) on pain and anxiety in pediatric surgical patients remains unexplored.

The trajectory of a patient with trauma is often complex and nonlinear. Real-time estimation of the mortality risk from prehospital care to discharge is critical for point-of-care decision-making and for benchmarking the quality of care. Conventional risk assessment systems in trauma are simple and data-sparse, leaving potential for harvesting available data for personalized risk assessments accounting for developing patient states.

Urban green spaces support psychological well-being, yet access is often limited for city residents. Virtual reality (VR) offers a promising indoor alternative, but evidence on sustained mental health effects of longer-term VR nature exposure remains limited.