ReviewRadiologie (Heidelberg, Germany)2025
[Artificial intelligence in radiology : Literature overview and reading recommendations].
Review in Radiologie (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
backgroundDue to the ongoing rapid advancement of artificial intelligence (AI), including large language models (LLMs), radiologists will soon face the challenge of the responsible clinical integration of these models.
objectivesThe aim of this work is to provide an overview of current developments regarding LLMs, potential applications in radiology, and their (future) relevance and limitations. MATERIALS AND
methodsThis review analyzes publications on LLMs for specific applications in medicine and radiology. Additionally, literature related to the challenges of clinical LLM use was reviewed and summarized.
resultsIn addition to a general overview of current literature on radiological applications of LLMs, several particularly noteworthy studies on the subject are recommended.
conclusionsIn order to facilitate the forthcoming clinical integration of LLMs, radiologists need to engage with the topic, understand various application areas, and be aware of potential limitations in order to address challenges related to patient safety, ethics, and data protection.
Indexed as
Identifiers
39904811What 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.