ArticleJMIR research protocols2021
Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach.
Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Artificial Intelligence (AI) and Healthcare Capabilities: A Systematic Review and Research Directions.F1000Research · 2025Pooled it
- Increasing Educator Resources for Mild Cognitive Impairment and Dementia Using a Knowledge-to-Action Framework: The Development of DementiaBank Grand Rounds.American journal of speech-language pathology · 2026Article
- Review
- AI-Enhanced Continuing Professional Development as an Evolving Sociotechnical System: Multimethod Theoretical Framework Development Study.JMIR medical education · 2026Article
- Healthcare professionals' perspectives on artificial intelligence in clinical practice: a systematic review of facilitators and challenges.Oxford open digital health · 2026Article
- Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study.Journal of medical Internet research · 2025Article
- Preparing Generation Z of Health Professions for Artificial Intelligence Revolution Through Hacking Education: An Interventional Study.Health science reports · 2025Article
- Evaluation of accuracy, quality, and readability of information on hypothyroidism provided by different artificial intelligence chatbot models.Frontiers in public health · 2025Article
- The Ubuntu Way: Ensuring Ethical AI Integration in Health Research.Wellcome open research · 2024Review
- The Adoption of AI in Mental Health Care-Perspectives From Mental Health Professionals: Qualitative Descriptive Study.JMIR formative research · 2023Article
- Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study.JMIR human factors · 2023Article
- Effect of a flipped classroom course to foster medical students' AI literacy with a focus on medical imaging: a single group pre-and post-test study.BMC medical education · 2022Article
- A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.International journal of environmental research and public health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSignificant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today's health care providers.
objectiveThe aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system.
methodsTo accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature.
resultsThe environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI.
conclusionsTechnologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30940.
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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.