Journal of Medical Internet Research
The leading peer-reviewed journal for digital medicine and health and health care in the internet age.
Editor-in-Chief:
Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada Rachele Hendricks-Sturrup, DHSc, MSc, MA, FACTS, Lead Editor; Research Director of Real-World Evidence, Duke-Margolis Institute for Health Policy, Washington, DC
Impact Factor 8.2 More information about Impact Factor CiteScore 10.4 More information about CiteScore
Recent Articles
AI-based clinical decision support systems are increasingly integrated into medical practice, creating hybrid decision-making processes in which physicians and AI systems jointly contribute to clinical judgments. Yet, how different forms of such AI support affect patients’ trust in hybrid medical decisions remains poorly understood.
Digital health interventions (DHIs), including telemedicine and AI-enabled health tools, are increasingly integrated into health care systems worldwide. While these technologies have the potential to improve access and efficiency, unequal access to resources and health capabilities may create disparities in their use. Evidence remains limited on how social and structural determinants shape population-level entry into DHI use in rapidly digitalizing health systems such as China.
Safe implementation of autonomous AI in medicine requires rigorous evaluation through clinical trials. The 7 guiding principles for ethical clinical research endorsed by the National Institutes of Health (NIH) provide an established framework for promoting scientific rigor and protecting the safety of human participants. However, clinical trials of autonomous AI raise novel ethical issues that require adaptation of these principles to account for effects that can vary across stakeholders and implementation contexts, including model performance across clinical settings. Incorporating expert perspectives on such challenges is critical to developing effective and ethically robust guidelines for autonomous AI clinical trials.
Health care providers are among the most trusted professionals, and they are rapidly adapting to AI integration in medicine. Authors Hou et al reviewed and synthesized qualitative studies of patient concerns regarding AI in health care. Themes included privacy, data security, and the “black box” complexity of AI decision-making; decreased trust in the physician-patient relationship and in the accountability of health systems; and equitable access, ethical regulation, and the displacement of human workers. Global and national health leaders can support health systems by establishing specific guidelines for informed consent about AI use in patient care. Similarly, health care leadership groups, in collaboration with AI developers, should establish checkpoints for physician review and specific loci for accountability prior to clinical use. Equitable access, ethical regulation, and the preservation of access to human providers, particularly when empathetic holistic care is paramount, will all impact the future of patient trust in physicians and health care systems. Centering our actions on the concerns of patients provides a road map to improve health care delivery, with AI at the service of patients and physicians.
Clinical gait assessment is essential for monitoring functional progress in children with cerebral palsy (CP); however, traditional visual observation remains inherently subjective and labor-intensive. Although AI-supported video gait assessment may provide more objective and automated outputs, many tools remain difficult to integrate into routine clinical workflows.
The standardized extraction of postoperative complications from unstructured routine clinical documentation remains a major unresolved challenge in digital surgery and health informatics. Although the Clavien-Dindo classification is the established standard for grading postoperative complications, its application in routine clinical documentation is largely implicit and unstructured, limiting scalable quality assessment in surgical care.
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