ReviewCardiovascular and interventional radiology2026
The Role of AI in Clinical Trial Design and Scientific Writing.
Review in Cardiovascular and interventional radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Clinical Trials in Interventional Oncology: A Field Growing Up Fast.Cardiovascular and interventional radiology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is transforming the landscape of clinical research and scientific writing, offering innovative solutions to address inefficiencies in trial design, patient recruitment, and manuscript development. This review explores applications of artificial intelligence in patient matching, endpoint design and predictive trial outcomes, and real-time patient monitoring. It also discusses its role in assisting with literature review, generating content, and refining language in scientific writing, especially for nonnative English speakers. Challenges such as data standardization, explainability, and ethical concerns are highlighted alongside emerging regulatory frameworks to ensure transparent and responsible artificial intelligence integration. By examining its current capabilities and future potential, this article underscores the transformative role of artificial intelligence in enhancing efficiency, reducing costs, and advancing innovation in medical research.
Indexed as
Identifiers
41053471What Socratic 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 Socratic graph.