ArticleInternational journal of computer assisted radiology and surgery2026
Lexomics, or why to extract relevant information from radiology reports through LLMs.
Article in International journal of computer assisted radiology and surgery, 2026. 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
4 authors.
Funding
Abstract
purposeThe application of large language models (LLMs) to radiology reports aims to enhance the extraction of meaningful textual data, improving clinical decision-making and patient management. Similar to radiomics in image analysis, lexomics seeks to reveal hidden patterns in radiology reports to support diagnosis, classification, and structured reporting.
methodsLLMs and natural language processing (NLP) algorithms analyze radiology reports to extract relevant information, refine differential diagnoses, and integrate clinical data. These models process structured and unstructured text, identifying patterns and correlations that may otherwise go unnoticed. Applications include automated structured reporting, quality control, and enhanced communication of incidental and urgent findings.
resultsLLMs have demonstrated the ability to assist radiologists in real-time, standardizing classifications, improving report clarity, and enhancing the integration of radiology reports into electronic health records (EHRs). They support radiologists by reducing redundancies, structuring free-text reports, and detecting clinically relevant insights. Unlike radiomics, lexomics requires minimal computational power, making it more accessible in clinical settings.
conclusionLexomics represents a significant advancement in AI-driven radiology, optimizing report utilization and communication. Future research should focus on addressing challenges such as data privacy, bias mitigation, and validation in diverse clinical scenarios to ensure ethical and effective implementation in radiological practice.
Indexed as
Identifiers
40982191What 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.