ArticleJournal of the American Medical Informatics Association : JAMIA2026
Gaps in artificial intelligence research for rural health in the United States: a scoping review.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- An exploratory survey of barriers and enablers of AI adoption in rural and urban dental practices in Mecklenburg-Western Pomerania, Germany.Scientific reports · 2026Article
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Explainability in context: calibrating appropriate trust and reliance in artificial intelligence.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- Disparities in Lung Cancer Health Outcomes and Access to Lung Cancer Screening Between Rural and Urban Areas in the U.S.Cancers · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Monitoring and evaluation of an artificial intelligence-enhanced wound care intervention in a rural health network: defining stakeholder expectations and shared priorities.Frontiers in digital health · 2026Article
- The state of digital health adoption in Nevada: A narrative review of infrastructure, policy, and health equity.Digital healthReview
Corrections and comments
- Update of
Authors and funding
2 authors.
Funding
Abstract
objectiveArtificial intelligence (AI) has impacted healthcare at urban and academic medical centers in the US. There are concerns, however, that the promise of AI may not be realized in rural communities. This scoping review aims to determine the extent of AI research in the rural US. MATERIALS AND
methodsWe conducted a scoping review following the PRISMA guidelines. We included peer-reviewed, original research studies indexed in PubMed, Embase, and WebOfScience after January 1, 2010 and through April 29, 2025. Studies were required to discuss the development, implementation, or evaluation of AI tools in rural US healthcare, including frameworks that help facilitate AI development (eg, data warehouses).
resultsOur search strategy found 26 studies meeting inclusion criteria after full text screening with 14 papers discussing predictive AI models and 12 papers discussing data or research infrastructure. AI models most often targeted resource allocation and distribution. Few studies explored model deployment and impact. Half noted the lack of data and analytic resources as a limitation. None of the studies discussed examples of generative AI being trained, evaluated, or deployed in a rural setting. DISCUSSION: Practical limitations may be influencing and limiting the types of AI models evaluated in the rural US. Validation of tools in the rural US was underwhelming.
conclusionWith few studies moving beyond AI model design and development stages, there are clear gaps in our understanding of how to reliably validate, deploy, and sustain AI models in rural settings to advance health in all communities.
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.