Articlenpj health systems2026
Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform.
Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- An Agentic Multimodal Sensing Architecture for CT-Guided Wearable and Respiratory Monitoring in Oncology Care.Sensors (Basel, Switzerland) · 2026Article
- Predicting health and disease: a conceptual framework for AI in preventive and precision medicine.BMJ health & care informatics · 2026Review
- NIPTE 2030: The Nation's Digital Trust Anchor for Pharma 5.0.Pharmaceutical research · 2026Article
- Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Artificial intelligence (AI) holds promise for healthcare, but real-world implementation remains difficult. The Mayo clinic platform (MCP) addresses this by providing scalable, multi-institutional, de-identified data and analytical tools. Through four research projects, we demonstrate MCP's ability to support efficient cohort identification, AI model development, and real-world evidence generation. MCP enables broader accessibility and standardization compared to institutional EHRs, positioning it as a powerful platform for advancing translational research and precision medicine.
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.