ReviewRadiologie (Heidelberg, Germany)2025
[Technical foundations of large language models].
Review in Radiologie (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- From algorithms to operating room: can large language models master China's attending anesthesiology exam? A cross-sectional evaluation.International journal of surgery (London, England) · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge language models (LLMs) such as ChatGPT have rapidly revolutionized the way computers can analyze human language and the way we can interact with computers.
objectiveTo give an overview of the emergence and basic principles of computational language models.
methodsNarrative literature-based analysis of the history of the emergence of language models, the technical foundations, the training process and the limitations of LLMs.
resultsNowadays, LLMs are mostly based on transformer models that can capture context through their attention mechanism. Through a multistage training process with comprehensive pretraining, supervised fine-tuning and alignment with human preferences, LLMs have developed a general understanding of language. This enables them to flexibly analyze texts and produce outputs of high linguistic quality.
conclusionTheir technical foundations and training process make large language models versatile general-purpose tools for text processing, with numerous applications in radiology. The main limitation is the tendency to postulate incorrect but plausible-sounding information with high confidence.
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.