ArticleBioinformation2025
Artificial intelligence in radiology and diagnostic imaging.
Article in Bioinformation, 2025. 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.
- Evaluation of multimodal large language models for pneumothorax assessment in real-world clinical scenarios.BMC pulmonary medicine · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence applications in radiology and diagnostic imaging encompass machine learning algorithms and deep neural networks that enhance image acquisition, analysis, and interpretation. Automated detection tools improve lesion conspicuity and quantification in modalities such as computed tomography, magnetic resonance imaging, and ultrasound, yielding greater diagnostic accuracy and consistency. AI-driven workflow optimization streamlines image reconstruction and prioritizes critical findings to accelerate clinical decision making and reduce reporting turnaround times. Integration of radiomics and predictive modeling facilitates noninvasive phenotyping of tissue characteristics and risk stratification for personalized patient management. Despite promising outcomes, challenges remain in algorithm generalizability, integration with picture archiving and communication systems, and regulatory approval pathways. Ethical considerations including data privacy, algorithmic bias, and explainability must be addressed to ensure safe and equitable deployment. This narrative review synthesizes current developments, evaluates clinical efficacy, and outlines future directions for AI in radiology and diagnostic imaging.
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.