ReviewMayo Clinic proceedings. Digital health2024
Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience.
Review in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials.ESMO real world data and digital oncology · 2026Article
- Leveraging Machine Learning and Artificial Intelligence in Cancer Diagnostics Imaging: A Systematic Review.Cureus · 2025Review
- Article
- Organizational readiness: The road to adoption of artificial intelligence in electrophysiology.HeartRhythm case reports · 2025Article
- Critical AI Health Literacy as Liberation Technology: A New Skill for Patient Empowerment.NAM perspectives · 2025Article
- Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight.Academic pathologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) promises to revolutionize health care. Early identification of disease, appropriate test selection, and automation of repetitive tasks are expected to optimize cost-effective care delivery. However, pragmatic selection and integration of AI algorithms to enable this transformation remain challenging. Health care leaders must navigate complex decisions regarding AI deployment, considering factors such as cost of implementation, benefits to patients and providers, and institutional readiness for adoption. A successful strategy needs to align AI adoption with institutional priorities, select appropriate algorithms to be purchased or internally developed, and ensure adequate support and infrastructure. Further, successful deployment requires algorithm validation and workflow integration to ensure efficacy and usability. User-centric design principles and usability testing are critical for AI adoption, ensuring seamless integration into clinical workflows. Once deployed, continuous improvement processes and ongoing algorithm support ensure continuous benefits to the clinical practice. Vigilant planning and execution are necessary to navigate the complexities of AI implementation in the health care environment. By applying the framework outlined in this article, institutions can navigate the ever-evolving and complex environment of AI in health care to maximize the benefits of these innovative technologies.
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.