ReviewRadiologie (Heidelberg, Germany)2025
[Integration of large language models into the clinic : Revolution in analysing and processing patient data to increase efficiency and quality in radiology].
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 3 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
3 citing papers in PubMed.
- [Keeping track of things: large language models for patient synopses : Source-bound system for clinical information systems].Radiologie (Heidelberg, Germany) · 2026Review
- Article
- From dictation to diagnosis: enhancing radiology reporting with integrated speech recognition in multimodal large language models.European radiology · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge Language Models (LLMs) like ChatGPT, Llama and Claude are transforming healthcare by interpreting complex text, extracting information, and providing guideline-based support. Radiology, with its high patient volume and digital workflows, is a ideal field for LLM integration.
objectiveAssessment of the potential of LLMs to enhance efficiency, standardization, and decision support in radiology, while addressing ethical and regulatory challenges. MATERIAL AND
methodsPilot studies at Freiburg and Basel university hospitals evaluated local LLM systems for tasks like prior report summarization and guideline-driven reporting. Integration with Picture Archiving and Communication System (PACS) and Electronic Health Record (EHR) systems was achieved via Digital Imaging and Communications in Medicine (DICOM) and Fast Healthcare Interoperability Resources (FHIR) standards. Metrics included time savings, compliance with the European Union (EU) Artificial Intelligence (AI) Act, and user acceptance.
resultsLLMs demonstrate significant potential as a support tool for radiologists in clinical practice by reducing reporting times, automating routine tasks, and ensuring consistent, high-quality results. They also support interdisciplinary workflows (e.g., tumor boards) and meet data protection requirements when locally implemented. DISCUSSION: Local LLM systems are feasible and beneficial in radiology, enhancing efficiency and diagnostic quality. Future work should refine transparency, expand applications, and ensure LLMs complement medical expertise while adhering to ethical and legal standards.
Indexed as
Identifiers
40072530What 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.