ReviewDiagnostic and interventional radiology (Ankara, Turkey)2024
Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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
110 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of large language models for providing automated feedback in medical imaging education: a systematic review.Frontiers in medicine · 2026Pooled it
- Evaluating Large Language Model-Assisted Emergency Triage: A Comparison of Acuity Assessments by GPT-4 and Medical Experts.Journal of clinical nursing · 2026Observational
- Can vision-language models estimate patient age? evidence from panoramic radiographs.Clinical oral investigations · 2026Article
- Privacy Leakage in Federated Learning in Radiology Reports: Comparative Evaluation of Tokenizer and Batch-Size Privacy Risks.JMIR medical informatics · 2026Article
- Comparison of Multimodal Large Language Models and Oral and Maxillofacial Radiologists in the Detection of Incidental Findings on Panoramic Radiographs: A CBCT-Referenced Diagnostic Accuracy Study.Healthcare (Basel, Switzerland) · 2026Article
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Large language models in neuroradiology: an international survey of awareness, applications, and concerns.Neuroradiology · 2026Article
- Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians.JMIR medical education · 2026Article
- Performance of multimodal large language models for the detection and characterization of bone lesions on radiographs.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Artificial Intelligence Reading of Postoperative Cochlear Implant X-Rays.Journal of clinical medicine · 2026Article
- Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.Journal of magnetic resonance imaging : JMRI · 2026Article
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- The role of large language models in the writing of surgical reviews: fact or fantasy?Langenbeck's archives of surgery · 2026Article
- Reply: Evaluating the reference accuracy of large language models in radiology: a comparative study across subspecialties.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models.European radiology · 2026Article
- Large Language Model-Generated Differential Diagnoses in Radiology Education: Comparison with a Standard Casebook.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic and Prognostic Non-Invasive Markers in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2026Review
- Large language models in neuro-oncology: promise, pitfalls, and the path forward.European radiology · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.
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.