ArticleJMIR AI2025
ETHICS of AI Adoption and Deployment in Health Care: Progress, Challenges, and Next Steps.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and evaluation framework for IoB-based adaptive health coaching systems.Frontiers in digital health · 2026Pooled it
- Review
- Generative artificial intelligence adoption and use in teaching and training healthcare professionals in higher education in the United States: a cross-sectional study.BMC medical education · 2026Article
- Healthcare Providers' Perspectives on Generative Artificial Intelligence (GenAI) Adoption, Adaptation, Assimilation, and Use in the United States.Healthcare (Basel, Switzerland) · 2026Article
- The conflict between need and fear: how privacy concerns moderate the influence of depression on university students' acceptance of AI music therapy.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Generative artificial intelligence (GenAI) is increasingly being integrated into health care, offering a wide array of benefits. Currently, GenAI applications are useful in disease risk prediction and preventive care, diagnostics via imaging, artificial intelligence (AI)-assisted devices and point-of-care tools, drug discovery and design, patient and disease monitoring, remote monitoring and wearables, integration of multimodal data and personalized medicine, on-site and remote patient and disease monitoring and device integration, robotic surgery, and health system efficiency and workflow optimization, among other aspects of disease prevention, control, diagnosis, and treatment. Recent breakthroughs have led to the development of reliable and safer GenAI systems capable of handling the complexity of health care data. The potential of GenAI to optimize resource use and enhance productivity underscores its critical role in patient care. However, the use of AI in health is not without critical gaps and challenges, including (but not limited to) AI-related environmental concerns, transparency and explainability, hallucinations, inclusiveness and inconsistencies, cost and clinical workflow integration, and safety and security of data (ETHICS). In addition, the governance and regulatory issues surrounding GenAI applications in health care highlight the importance of addressing these aspects for responsible and appropriate GenAI integration. Building on AI's promising start necessitates striking a balance between technical advancements and ethical, equity, and environmental concerns. Here, we highlight several ways in which the transformative power of GenAI is revolutionizing public health practice and patient care, acknowledge gaps and challenges, and indicate future directions for AI adoption and deployment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.