ReviewBJR artificial intelligence2024
Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.
Review in BJR artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Standardization of bone mineral density and microstructure from high-resolution CT-scans of the spine in a multicenter setting.Zeitschrift fur medizinische Physik · 2026Article
- Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.Cureus · 2025Review
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic computed tomography generation.Physics and imaging in radiation oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) has enabled tasks in radiology, including tools for improving image quality. Recently, new hotspots have emerged, such as intra- or inter-modal image translation, task-specific image synthesis, and text generation. Advances in generative AI have facilitated the move towards low-dose, cost-effective, and high-quality radiological image acquisition. Large language models can aid radiologists by generating professional answers and facilitating patient-physician communications. However, radiologists must be aware of potential inaccuracies in the generated content and should only use such tools after rigorous validation of their performance.
Indexed as
Identifiers
What 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.